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Architecture, often regarded as a three-dimensional art form, relies on a multitude of abstractions during the design process. In this context, architectural elements are effectively represented through vector-based data processing techniques, offering a canvas for creative exploration. To further our understanding of spatial configurations and generate multiple design insights, mathematical structures known as graphs come into play. Graph theory, a fundamental component of this process, it is a mathematical field that studies relationships and connections between objects using vertices and edges, finding applications in computer science, network analysis, and more finds application in architectural design. This study delves into the application of graph theory in the analytical exploration of urban networks, with a focus on tourism parameters in the picturesque town of Pondicherry, Tamil Nadu, India. Traditionally, urban network analysis seeks to determine the shortest distance between origin and destination, a metric rooted in accessibility. However, in leisure destinations, a novel approach involving an optimized distance-based algorithm is introduced. This innovative methodology aims to facilitate efficient access to all tourist destinations within the spatial extent while minimizing discrete physical distances. To illustrate this concept, we take the iconic French Quarters in the White Town of Pondicherry as a case study. By applying the optimized distance-based algorithm, we endeavor to uncover the most efficient route for tourists to navigate and leisurely explore the area. The outcome of this analysis reveals a path graph structure, which, when compared to a circular graph structure, demonstrates superior efficiency in guiding tourists through the space. The implications of this tourist route extend beyond the realm of tourism itself. It has the potential to enhance the organization of tourist activities, allowing stakeholders to manage and regulate the flow of visitors. Additionally, it presents an opportunity to promote and develop the region's often-underestimated tourist destinations, which can, in turn, have a positive impact on the local economy. In summary, the integration of graph theory into architectural and urban design methodologies enriches the approach to tourism and offers a holistic perspective on spatial optimization. [ABSTRACT FROM AUTHOR]

The fast development of the Internet has raised the number of government-related websites and the variety of e-services available. Despite tourism's critical significance in supporting the national economy, relatively few researches have been conducted on the influence of tourism website quality dimensions on electronic loyalty among citizens (e-loyalty), particularly in the Jordanian domestic tourism context. The objective of this research is to identify the factors that impact the e-loyalty of citizens toward the e-government services offered by the Ministry of Tourism and Antiquities in Jordan, considering the mediating influence of citizens' e-satisfaction and e-trust, primarily following Oliver's (1999) e-loyalty model. The survey questionnaire was adopted as a main strategy to collect and analyze primary data and to investigate the relationships between variables. Statistical processing was applied using IBM SPSS version 22 package and Amos version 25 for path analysis as the main statistical software package. The results indicated that citizens' e-loyalty toward the governmental tourism-related website in Jordan is moderate, demonstrating that citizens are generally comfortable with the e-services provided. Furthermore, the results suggest that e-satisfaction and e-trust both play a mediating role in the connection between dimensions of website quality (specifically, information quality and personalization) and the e-loyalty of citizens. This study contributes to the theory by combining the relationships among website quality dimensions, citizen e-trust, e-satisfaction, and e-loyalty, particularly in the unique setting of Jordan's domestic tourism sector. The results provide valuable insights for policymakers and tourism sector managers, aiding them in the implementation of information and communication technology (ICT) strategies that facilitate citizens' transactions and encourage them to participate effectively in e-government activities, thereby boosting the kingdom's economy and gross domestic product (GDP). The government also recommended enhancing awareness programs and employee training in order to enhance the e-services offered to citizens. [ABSTRACT FROM AUTHOR]

With the recovery of the tourism industry and the development of the field of artificial intelligence, the application of intelligent neural network technology to management systems for safety risk assessment is the choice of the times and an urgent real need for relevant practitioners. In this study, the BP neural network algorithm is used as a tool for safety evaluation of the tourism management system. The three-layer structure of the BP neural network and the role of nodes in it are introduced, the weight values and thresholds of nodes in each of the three layers are calculated, and the particle swarm algorithm is added to optimize the model. In the practical stage, the tourism data of a place in Switzerland was selected as the training data. After 12,859 iterations, the model achieved the best calibration error of 0.126. After 300 iterations of learning, the BP algorithm optimized with the PSO algorithm has a faster convergence rate, which indicates that the performance of the optimized algorithm has improved significantly and has the global search capability that was not available before, which significantly outperforms the FastText and LSTM models. With the increase in the number of samples of tourism data, macroaccuracy always remains above 80%, so it proves that the optimization algorithm used in this study is an effective and reliable model. [ABSTRACT FROM AUTHOR]

Information technology has been increasingly vital in the rapid expansion of the tourism industry economy. The new information technology has become a significant driving force and factor in the tourism industry's economic development. The application of new information technology to the advancement of modern tourist intelligence has emerged as an essential study area. Smart tourism, aided by informatization, can frequently provide more attractive economic benefits to tourism businesses. Tourism is being positioned as a new economic growth point and core business in an increasing number of cities. Various measures have been taken among the main body of the city to improve the tourism competitiveness of the city, so as to be in an advantageous position in the fierce market competition. Under this background, the research on urban tourism competitiveness has become a research hotspot in the development of modern tourism intelligence. This study used a combined PSO and neural network to assess the competitiveness of urban tourism. First, this paper offers a method that makes use of an improved IPSO. It changes the inertia weight dynamically and nonlinearly based on the particle fitness value. Simultaneously, the particle swarm technique is enhanced by combining the particle iterative cycle to boost position disturbance. Second, in order to build an IPSO-BP network, this work optimizes the initial weights and thresholds of the BP network based on IPSO. This work uses this network to evaluate the competitiveness of urban tourism, which can overcome the defects of traditional BP network. Third, this work conducts systematic experiments on the IPSO-BP method, and the experimental results confirm the superiority of this method in the evaluation of urban tourism competitiveness. [ABSTRACT FROM AUTHOR]

A country's economic development relies on different features such as export/import, industrial processes, and tourism. Rural tourism is a discussion-centric research field for analyzing its contribution to a country's economic growth. This field generates voluptuous data for tourists, expenditure, location, etc. analysis; the information increases over the years and the density of visiting tourists. Therefore, this article introduces an optimized reinforcement data analysis approach (ORDAA) for generating precise guidance information. This information is two-faced, namely, summarized data for tourist guidance and summarized data for the country's economic development. Data augmentation's steep rise and downfall are analyzed using reinforcement learning, wherein decision agents are precise for a relevant summary. The relevance is identified using associated development targets over varying years. Besides, the guidance information that identifies low tourist summary or nonachievable development targets is separately identified. The identified targets are analyzed using reinforcement agents for economic growth improvements compared to the previous tourist densities. This improves the focus on rural tourism sights and economic contributions to an optimal level. [ABSTRACT FROM AUTHOR]

With the increasing improvement of people's living standards, more and more tourists choose to travel independently, which puts forward higher requirements for the existing tourist guide system of scenic spots. Augmented reality (AR) is a technology that integrates computer-generated virtual information into real scenes. It provides more interactive modes, allowing visitors to have a stronger sense of immersion in the real world. The interactive navigation system is an analysis and processing system that recommends algorithms for tourists through the back-end server by collecting the user's registration information data. It provides tourists with reasonable travel itineraries. This can not only allow users to avoid the peak flow of people and save tourists' time, but also reduce the hidden dangers of scenic spots and improve the turnover rate of passenger flow. This paper aims to study an interactive guide system combined with augmented reality technology to provide tourism services for tourists in tourist attractions based on geographic locations. This paper proposes an improved probabilistic algorithm model to accurately locate tourists. (Probabilistic algorithms are also called randomization algorithms. Probabilistic algorithms allow the random choice of the next computational step during execution. In many cases, when the algorithm is faced with a choice during the execution process, the random choice is time-saving compared to the optimal one. Therefore, the probabilistic algorithm can greatly reduce the complexity of the algorithm.) At the same time, the paper designs an AR scenic spot tour guide system. It tests the performance of the system. The results show that the initial response speed of the improved probability algorithm is 0.69 s, the average response time is 4 s when the number of concurrent users is finally increased to 600, and the growth rate is about 0.006 s. The improved algorithm can obviously enhance the response speed. [ABSTRACT FROM AUTHOR]

With the swift development of tourism all around the world, it has become vital to improve the recommendation of useful travel information to tourists to assure their convenience and satisfaction. In this paper, we propose a novel multi-objective optimal travel route recommendation framework, which collects tourists' travel trajectories from their mobile phone signaling data. Then, the proposed framework preprocesses the mobile signaling data to transform raw trajectories into tourists' travel sequences. Subsequently, the framework finds the popular attractions and frequent travel routes from the travel pattern sequences by using a frequent pattern mining method. Finally, an improved ant colony optimization (ACO) algorithm with a novel extensible heuristic factor approach is adopted to search the multi-objective optimal travel routes according to the popularity of attractions and travel time of tourists. The experimental results indicate that the proposed framework is efficient in recommending multi-objective optimal travel routes considering tourists' travel time and attractions' popularity while ensuring that the recommended travel route is suitable. [ABSTRACT FROM AUTHOR]

The establishment of an intelligent, comprehensive, and all-encompassing information system for tourism management is the current trend in tourism informatization as a result of the continual development of modern information technology. Significant advancements in the field of VRGIS and its usage in research have been made as a result of the use of VRGIS to categorize, assess, plan, and manage tourism resources. The analysis of the recent development of VRGIS in tourism resource research is the first section of this work. This study examines and implements a mobile, computerized, and intelligent tourism service system that gives visitors a sense of the surrounding landscape using VRGIS. Three-dimensional mapping, environment detection, personal trajectory, and Weibo sharing are just a few of the system's many helpful features. While travelling, tourists can get services that are more intelligent and practical. The drawbacks of conventional geographic information systems include their reliance on sophisticated models, network limitations, and operational challenges. New software architecture is put in place to get rid of network restrictions, virtual reality peripherals are used to make operation more convenient, and system modeling is rebuilt using the TIN data model and model simplification. The results of experiments show improved refresh rates and peripheral expansion modules. The user experience is enhanced by this research. [ABSTRACT FROM AUTHOR]

Rural ecotourism is of great significance to the construction of new countryside, including promoting rural social progress, adjusting agricultural industrial structure, and increasing farmers' income. Starting with the basic concepts of rural ecotourism and new rural construction, this study compares and analyzes the development background and main characteristics of rural ecotourism at home and abroad and evaluates the development of rural ecotourism based on the Internet. This study takes Shahe City in Hebei Province as an example to develop rural ecotourism in the new rural construction and analyzes its tourism development status combined with the Internet+. Combined with its development orientation and basic conditions, this study summarizes the main measures for the development of rural ecotourism in Shahe City, Hebei Province, in order to improve the effectiveness of new rural construction. The results showed that the income of Shahe tourism increased by 31.5%, and the income of outbound tourism increased by 8.9%. This shows that the new rural construction can promote the development of rural ecotourism, increase the passenger flow, and improve the per capita income. The new rural construction and rural ecotourism development complement each other. [ABSTRACT FROM AUTHOR]

The Internet has penetrated into all fields. As the most dynamic "sunrise industry," tourism has also been swept into such a wave of Internet. In such an era of "information overload," how to find one's favorite attractions among the massive tourist attractions has become a difficult problem. In order to solve this problem, personalized recommendation technology is applied, among which collaborative filtering recommendation technology is one of the core technologies while the collaborative filtering algorithm still has problems. The research and analysis of the algorithm, this paper improves the technology for the problems of low recommendation accuracy that considers user interest changes. It for attribute scoring. It uses the multiattribute score of the item to calculate the user's overall evaluation score of each attribute of the item; for the change of user interest, a time function based on the Ebbinghaus forgetting law is introduced to calculate the user similarity. It is given a certain weight, that is, a time function, to ultimately ensure the accuracy of the recommendation. Exploring the tourism destination recommendation and marketing model based on the collaborative filtering algorithm can enrich the relevant theories of it on the one hand, and on the other hand, it can lay the foundation for building a real tourism recommendation website. [ABSTRACT FROM AUTHOR]

Southeast Asia is one of the most popular tourist destinations in the world, and its tourism model is mainly based on ecological and sustainable tourism characteristics. It is a special practical exploration of this region. However, with the rapid development of the tourism industry, the structural contradictions in the tourism industry have become increasingly prominent, coupled with the weak link of tourism development, making the development of tourism unbalanced. Therefore, this study proposed a platform-related algorithm based on machine learning to build the integration of eco-sustainable tourism and marketing in Southeast Asia and used a collaborative filtering algorithm and a route generation algorithm to recommend information or predict specific needs for specific tourists. The experimental results of this study showed that the difference between different loss functions of the improved transfer algorithm based on machine learning is not significant, which indicated that the method proposed in this study is robust to the choice of a loss function. Compared with the hinge loss function and the logarithmic loss function, the exponential loss function has the highest average classification accuracy of 0.91, of which the average accuracy of the logarithmic loss function is 0.85, and the average classification accuracy of the hinge loss function is 0.79. Therefore, applying the migration algorithm to the architecture of the platform is conducive to the stability of the dataset and to the better display of pages for tourists. [ABSTRACT FROM AUTHOR]

With the continuous development of Internet of Things technology, this technology is not limited to the laboratory but is more and more applied in the process of commercialization. Large-scale machine communication is one of the three most important scenarios for 5G applications. Its features include larger uplink access, smaller data packets, higher device energy consumption requirements, lower device transmission speed, higher latency, and bursty communication. With the development of the Internet of Things in the 5G era, interpersonal interaction experience has been further optimized and welcomed by the market and adopt Qt/embedded multilayer menu design mode to improve our computer system interaction to facilitate the operation and Maintenance system. The software function of the human-computer interaction system has reached the expected result. By constructing the application of a human-computer interaction system in the analysis of regional tourism competitiveness, we analyze the temporal and spatial changes of tourism competitiveness in four major economic regions in China and combine multiple linear regression and geographic detector models to analyze China's tourism competitiveness. On this basis, we propose to further enhance the tourism competitiveness of areas with weak tourism development infrastructure and encourage potential tourism development areas to complement each other with high-quality resources between regions. [ABSTRACT FROM AUTHOR]

Aiming at the lack of theoretical basis for the development of cultural tourism resources, an application method of artificial intelligence-based sensor technology in the recommendation model of cultural tourism resources is proposed. Sensor network is an application-based network. Compared with traditional wireless communication network, it has the characteristics of large node scale, self-organized multihop, unattended, and no communication infrastructure. Combined with artificial intelligence sensor technology, this paper attempts to construct an evaluation index system and evaluation model for cultural tourism resources, and uses this model to conduct a comprehensive evaluation of cultural ecotourism resources in the western region. The experimental results show that the E value of the evaluation result of cultural tourism resources in the western region is 6.346, which has a good development value. Secondly, from the specific evaluation results of each level, the western region has the highest cultural tourism standard, which has 6.605. Cultural tourism resources, landscape resources, and development conditions scored lower. Among them, cultural tourism resources and landscape resources have the highest score (7.186), and the economic and cultural field has the lowest score (6.092). Among the cultural tourism development conditions, the policy conditions are high (6.823), but the location conditions are very low, only 4.879 points. Therefore, it is found that the model takes cultural tourism landscape resources, cultural tourism environment resources, and cultural tourism development conditions as the content of cultural tourism resources evaluation, which is comprehensive and has strong hierarchy and pertinence; fuzzy comprehensive evaluation method has strong objectivity to determine the weight and the value classification; and weighting evaluation model of cultural tourism resources has rationality and generalizability and can provide a scientific basis for the classification and evaluation of cultural tourism resources and the planning and development of cultural tourism. [ABSTRACT FROM AUTHOR]

In order to improve the effect of intelligent development of tourism resources, this paper combines the Internet of Things technology and 5G technology to carry out intelligent development of tourism resources to improve the recommendation effect of modern tourism resources. Meanwhile, this paper analyzes the basic knowledge of array signal DOA estimation to get the definition of narrowband signal. Simultaneously, this paper proposes a subspace-based DOA estimation algorithm based on the actual needs of tourism resource management, and uses the observed array data to construct a covariance matrix. Then, this paper decomposes the covariance matrix, and then performs DOA estimation. In addition, this paper constructs an intelligent development system of tourism resources based on the Internet of Things and 5G technology. It can be seen from the experimental research that the intelligent development system of tourism resources based on the Internet of Things and 5G technology proposed in this paper has good tourism resource management and development effects. [ABSTRACT FROM AUTHOR]

In order to define the positioning of the urban brand image, design the urban brand image, integrate and optimize the communication channels, improve the public participation awareness, and enhance the core competitiveness of the city. In this paper, a personalized recommendation search engine based on big data identifies keywords input by urban users. And give more accurate results based on some relevant information that can be extracted. This paper analyzes how to make better use of big data for tourism destination brand image management, and the existing shortcomings, and puts forward relevant suggestions. The industries related to cultural creative design and tourism elements constitute an intertwined cultural and tourism industry chain, and data technology plays an important role in the cultural and tourism industry chain. Through the development of tourism, tourists will produce comprehensive and diversified consumption in the city. Based on the analysis of big data, it can provide strong decision support for the government and industry managers, and realize the image design and communication of the urban brand identification system. Through the big data platform, establish the brand management strategy, improve the communication content of the city's brand image, and timely feedback the opinions and suggestions of tourists on the tourism destination, so as to adjust the communication strategy of the tourism image according to the feedback information of tourists. The results show that the big data personalized recommendation system can achieve ideal results in urban brand value and urban tourism related factors. [ABSTRACT FROM AUTHOR]

This paper presents an in-depth analysis and study of traditional corridor construction and tourism system planning using intelligent sensing networks. This paper proposes a node deployment and coverage optimization method for HWSNs based on particle swarm optimization emperor butterfly algorithm and extreme learning machine, with the objectives of reducing node deployment cost, reducing coverage redundancy and voids, improving monitoring area coverage, and increasing the agility of data processing by 15.03%, efficiency by 12.15%, and stability by 11.31%. In the paper, the static parameter table of the sensor is stored inside the smart sensor or stored on the IoT cloud platform to achieve automatic registration of the static parameters of the smart sensor. According to the characteristics of dynamic parameters of smart sensors, an autonegotiation algorithm is designed to realize the dynamic parameters of smart sensors to be determined by autonegotiation and complete registration without human intervention, which includes four parts: utility function, concussion protocol, proposal generation strategy, and response mechanism. The intelligent sensing system is composed of three parts: smart sensor, IoT gateway, and IoT cloud platform; the functions of each part in the system are analyzed, and MAR and STM32F4 are selected as the core board of smart sensor and IoT gateway. And for the problems of time-consuming and tedious access to smart sensors in the system, we propose to study the adaptive sensor UART serial port parameters and automatic registration of sensor parameters. Based on the analysis of the current environment and resource characteristics of the traditional corridor, the current situation of ecotourism resources of the traditional corridor is sorted out, an evaluation index system is established to evaluate ecotourism resources, and the current development basis is analyzed from four aspects: natural environment, tourism resources, product development, and infrastructure. Finally, to promote the sustainable development of the ecotourism corridor in the Chengdu section of the southern foot of the Qinling Mountains, the planning and development strategies to match the ecotourism corridor in the Chengdu section of the southern foot of the Qinling Mountains are proposed in four aspects: transportation organization optimization, ecological landscape enhancement, support system construction, and regional tourism development. [ABSTRACT FROM AUTHOR]

As a hot issue of current research, tourism information service has higher and higher requirements for intelligent construction. Tourism service recommendation is the embodiment of smart tourism. However, there are still obvious deficiencies in solving the problem of Internet information load and improving user experience. Through functional analysis, architecture design, selection of relevant development frameworks, and improvement of collaborative filtering algorithms, a stable, reliable, high-performance, multi-functional intelligent travel recommendation system can be developed that can complete personalized recommendations. It can achieve the purpose of improving the efficiency and accuracy of recommendation and recommending tourism-related information to users in a targeted manner. Analyze the test plan and test the recommended algorithm module. In the case of different concurrency and database levels, the system response time is 0.9 s and 1 s, respectively. And in the case of high throughput, the system response time is 1.5 s, indicating that the system is running stably. It not only tested the storage and calculation of big data but also improved the usability of the travel information service recommendation system and the user experience of the system. [ABSTRACT FROM AUTHOR]

With the rapid development of the Internet and tourism, the Internet has been widely used in the tourism industry. Tourism enterprises and tourists use the Internet to publish and obtain travel-related information. Educational tourism is a new type of tourism activity. As a combination of "tourism + education," it has gradually attracted the attention of tourists. With its convenience, fast speed, and low barrier, tourism text data provide great convenience for tourists' sentiment calculation and have become one of the main sources of big data for tourism. However, the reviews of educational tourism have a lot of redundant information and complex sentence patterns, leading to a relatively low classification accuracy of the existing sentiment analysis algorithms. In order to effectively obtain the implicit semantic information of short text reviews for sentiment orientation recognition, a sentiment classification model for educational tourism online reviews based on parallel CNN and LSTM with multichannel attention mechanism is proposed. Firstly, Word2Vec technique is used, and based on noise word filtering, the feature words of educational tourism reviews are extracted to preprocess the input data set. Then, parallel CNN and LSTM are used to extract text local information and contextual features, and a multichannel attention mechanism is used to extract the attention values from the LSTM output. Finally, the output information of the multichannel attention mechanism is fused to effectively extract text features and focus on important words. The experimental results show that compared with other advanced methods, the proposed algorithm achieves improvements in terms of precision, recall, and F1 value and improves the AUC performance. It will help the educational tourism bases to carry out targeted development and construction in response to tourists feedback, enhance the sense of gain and happiness of tourists in educational tourism activities, improve tourism experience, and promote the rapid development of high-quality educational tourism. [ABSTRACT FROM AUTHOR]

The environmental effect of the meetings, incentives, conventions, and exhibitions (MICE) industry is as extensive as its economic impact. Visitors attending events use a wide range of service providers, including airline car rental firms, restaurants, hotels, theaters, and tour operators. Traditionally used tourism demand forecasting approaches rely heavily on univariate time series and multivariate regression models. Although these function-based prediction systems have demonstrated some effectiveness in forecasting tourism, they are unable to accurately capture the link between tourist demand and supply as a feed-forward neural network does (FFNN). Research has shown that an FFNN can outperform regression and time-series algorithms when it comes to forecasting tourism data. This research, for the first time, expands the use of neural networks in tourist demand creation by combining a hybrid FFNN and chimp optimization learning algorithm (i.e., FFNN-ChOA) into a nonlinear tourism demand dataset. In terms of predicting accuracy, FFNN-ChOA surpasses traditional backpropagation neural networks, regression models, and time-series models. [ABSTRACT FROM AUTHOR]

The optimization of the travel route for a round-trip is not only a customized demand made by a large number of independently guided tourists but also an essential practical issue for the development of tourism management and tourism businesses. In light of this, the study presents a genetic algorithm (GA) as a potential solution to the problem of how to visit a number of tourist destinations within a constrained area in order to quickly determine the shortest tourist route. To traverse areas or regions with the least amount of physical exertion, select the correct and shortest route. Examining all potential routes from the starting point to the destination will allow you to determine the quickest route. A condensed explanation of the enhanced GA is provided to start. The second step is to analyze the model's construction and solution in detail. Next, an enhanced genetic algorithm (IGA) is utilized to determine the optimal travel route for visiting a variety of tourist attractions. In accordance with the optimal travel route, the required number of days and specific travel arrangements are then estimated. In conclusion, the GA is optimized, and a simulation examination of each individual's average path convergence is conducted. The results of the experiments indicate that the IGA can be effectively applied to the path planning of multiple scenic locations, the selection of the shortest travel route, the reduction of travel expenses, and the saving of travel time. This has important implications for both research and practical applications, as well as a high research significance and practical value. [ABSTRACT FROM AUTHOR]

With the development of modern economy and society, people have higher and higher requirements for spiritual needs request. The demand of traveling is a key growth point of the people's spiritual needs. Forest tourism has become an important part of the forestry modernization. Its reasonable development can provide a site for urban residents' leisure and health preservation, and help the revitalization of local villages and economic development. However, there are few studies on the evaluation methods of forest tourism land resources potential development around the world nowadays, and the planning and development of forest land resources are also lack of scientific guidance. The research work of this paper is carried out based on the realistic problems above, through analyzing the characteristics of the land resources of forest tourism, carding the main indicators of forest land resources development value, and fuzzy analytic hierarchy process (AHP) is studied to evaluate its development potential. The relevant conclusions can provide a theoretical guidance for the forest land resources development and planning, which will improve the development quality and effect of forest land resource. [ABSTRACT FROM AUTHOR]

There are few studies on forest smart tourism currently, and most smart tourist service models are only studied and analyzed theoretically. Based on this, the Internet of Things (IoT), mobile communications technology, and smart tourism related ideas are discussed in depth in this study, followed by the construction of a forest smart tourist service model. The forest smart tourism evolutionary game model is then built based on the benefit relationships of various tourist sectors. Finally, the effect of the forest smart tourist service model and the forest smart tourism service evolutionary game model is evaluated using simulation experiments. The findings show that (a) daily passenger flow data from scenic spots in June was scattered, but the overall estimate gives that the daily passenger flow was more than 800; (b) daily passenger flow data from scenic spots in December was scattered, but overall estimate shows that the daily passenger flow was less than 300; and (c) the average profit value of the game model was 3.5, 3, and 6, respectively, under various circumstances. To summarize, the model provided in this paper is reliable and appropriate for optimizing forest smart tourist services. Through the constructed model, this work seeks to give theoretical reference for further boosting the development potential of forest recreation. [ABSTRACT FROM AUTHOR]

Food safety issues are inextricably linked to people's lives and, in extreme cases, endanger public safety and social stability. People are becoming increasingly concerned about food safety issues in a modern society with high-quality economic development. People's incomes are increasing day by day as the economy continues to grow, and the tourism industry has grown by leaps and bounds. However, many problems arose, such as the issue of food safety in tourism. Tourism food safety issues affect not only the development of the food industry but also the development of tourism. Food safety oversight of tourist attractions has always been a relatively concerning issue in the country, and it is also something that the general public is concerned about. It can be said that food safety supervision of tourist attractions is the most important thing in food safety supervision. In this context, it becomes an important task to evaluate the safety of tourist food. This work proposes a multiscale convolutional neural network (AMCNN) combined with neural networks and attention layers to realize the safety and quality evaluation of tourist food. The algorithm uses the lightweight Xception network as a basic model and utilizes multiscale depth-separable convolution modules of different sizes for feature extraction and fusion to extract richer food safety feature information. Furthermore, the convolutional attention module (CBAM) is embedded on the basis of the multiscale convolutional neural network, which makes the network model focus more on discriminative features. [ABSTRACT FROM AUTHOR]

In order to make up for the shortage of smart tourism construction highlighted by public tourism safety accidents due to the bearing capacity of scenic spots, this paper proposes a tourism area capacity early warning system based on the Internet of Things technology. By combining the main characteristics of a park and a place, this paper determines the composition of its tourism capacity and the corresponding calculation methods. At the same time, in order to control the number of tourists in peak hours within a reasonable range, the peak constraint method is proposed to improve the traditional algorithm of daily space capacity; then this paper calculates the tourism capacity of a park and a place by combining the relevant data obtained by investigation and observation methods. The experimental results show that according to the given number, the instantaneous tourism capacity of a park can be accurately calculated as 11841 person times/day and the daily tourism capacity as 21313 person times/day. The instantaneous tourism capacity of a certain place is 1066 person times/day, and the daily tourism capacity is 9594 person times/day. The construction of the early warning system of the tourism area based on the Internet of Things can effectively solve the problems such as public tourism safety accidents caused by the problem of carrying capacity. It will play an important role in the customized services of tourists, the innovation of scenic spot business processes, and the integration of tourism enterprise resources and provide data support for the early warning system. [ABSTRACT FROM AUTHOR]

With the continuous improvement of people's living standards, the form of tourism is gradually changing. In the past two years, people's demand for tourism has also changed from traditional ornamental tourism to wellness tourism, which has provided a development opportunity for rural tourism. At the same time, the government has also begun to focus on the development of tourism with rural characteristics to promote the development of rural areas. However, health tourism still has certain defects, and the development of rural planning is still advancing and improving. Leisure agriculture is an area where agriculture, tourism, and service industries intersect, and it is the link that maintains the common development of the three. Applying the concept of health tourism to the planning and design of leisure agricultural parks can make people satisfied in tourism. The pursuit of health and wellness also drives the development of the rural economy. With the progress of the economy and the continuous improvement of people's living standards, the traditional travel mode is gradually changing towards the direction of informatization. The traditional way of travel-by-travel agencies planning travel routes is no longer the first choice for people to travel. On the one hand, the rapid development of the Internet brings convenience to people, and on the other hand, it also brings a lot of information to the public. [ABSTRACT FROM AUTHOR]

The purpose is to find out the problems existing in the consumption economy structure of the scenic spots and to promote the rationalization of the consumption economy of the scenic spots. Based on the analysis of the applicability of the backpropagation neural network (BPNN) model, it uses BPNN to analyze the economic development level of Overseas Chinese Town East (OCT East). Firstly, the weight of each index is determined by the Analytic Hierarchy Process (AHP), and the expected value of the comprehensive evaluation is obtained. Secondly, to ensure the validity of the evaluation model for the development level of the tourism complex, the BPNN model is trained and tested to enable it to be applied to the evaluation of the economic development level of OCT East. The development level of OCT East from 2012 to 2021 is divided into three stages: high, higher, and lower. The development characteristics and existing problems of the OCT East are analyzed, and the optimization strategy of the consumption economy of the scenic spots is put forward in a targeted manner. The research results manifest, that from 2012 to 2021, the development level index of OCT East increased from 0.2457 to 0.5304, and it was in a state of steady growth. In 2019, the development level index reached 0.6497, and it was upgraded to "high-level," but the average development level index of OCT East was only 0.5662, and there was a lot of room for improvement. According to the divided evaluation indicators, the development level of OCT East is evaluated. In 2012, the development level was low. From 2013 to 2018, it was at a high level, and from 2019 to 2021, it was a high level of development. By studying the Tourism Consumption Structure (TCS) of scenic spots in the OCT East, the research method of the consumption economic structure has been expanded. Therefore, it not only provides a reference for optimizing the consumption of scenic spots, but also contributes to the progress of the social tourism economy. [ABSTRACT FROM AUTHOR]

In recent years, under the background of the national development strategy of rural revitalization, rural tourism has become a new way of tourism. With the upsurge of rural tourism, there has also been a wave of "homestay fever" across the country. Country house tourism is a personalized rural tourism method that has emerged with the development of our country's tourism industry in recent years. However, in the context of the surge in the number of homestays in rural tourism destinations, homogeneous competition, simple replication, low-price competition, and other quality development problems have become increasingly prominent. In order to realize the high-quality development of country houses and meet the differentiated needs of customers, it is necessary to explore the dimensions of tourists' perceived value of country houses from the perspective of tourists, to study the gap between tourists' expectations and actual perceptions, and from the perspective of tourists' value perception to make suggestions for improvement. This paper takes the theory of customer perceived value as the theoretical basis, and on the basis of referring to the relevant literature, combined with the actual situation of the homestay in Luci Village, to construct an index system of the perceived value of tourists in the country house. Use IPA (Importance-Performance Analysis) to identify the difference between the actual performance and expected importance of tourists' perceived value, use questionnaires and statistical software to analyze the difference in guest satisfaction and importance, and put forward key improvement goals to improve the quality of homestay development in the future. The effectiveness of the method proposed in this paper is verified through the case of a country house in Luci Village, Tonglu County. [ABSTRACT FROM AUTHOR]

Rural tourism, as a vital component of tourism, is critical to the development of rural economies, farmers' income, rural civilization, new rural construction, and urban-rural interaction. Simultaneously, as the size and complexity of data sets grow larger, how to improve the efficiency of association rule algorithms for mining large data sets has become a hot topic in association rule mining. Rural tourism development that is cultural and creative not only contributes to rural revitalization, but also to the preservation and inheritance of rural culture. The Apriori algorithm is the most widely used and influential algorithm for mining Boolean association rules, and the majority of current algorithms are extensions of the Apriori algorithm. Demand, supply, marketing, and support forces of rural tourism, which are the core driving force of rural tourism development, are formed by the basic needs of each subsystem of rural tourism. One of the main methods is to promote the sustainable and healthy development of rural tourism in accordance with the nature, characteristics, and laws of rural tourism destination construction, in order to create a dynamic system for long-term development and establish a rural tourism development dynamic system. The study of rural tourism driving factors and their system optimization is proposed in this paper. The main tourism dynamic system is adopted by the association rule algorithm of Apriori, the driving factors of rural tourism development are analyzed in the paper, and the system optimization method is proposed, all based on the Apriori algorithm. In terms of support, the Apriori algorithm is 0.436 higher than the CD algorithm and 0.568 higher than the SVM algorithm, and the Apriori algorithm can greatly reduce database size and improve record reading speed. As a result, the findings of this paper can be used to improve the spatial layout of rural tourism and to develop urban-rural tourism. [ABSTRACT FROM AUTHOR]

In recent years, the tourism industry has grown rapidly around the world as an emerging force, especially in China, which has become the world's leading tourism country in recent years, and its tourism revenue also occupies a good weight in the country's total income. The number of tourists every year shows a very high growth rate, which not only improves the tourism economy but also brings great pressure to the management of various tourist attractions. In this context, many tourist attractions are actively developing innovative applications of new technologies, hoping to effectively improve their management efficiency. In these experiments, the speech big data analysis technology has achieved remarkable achievements, so it is necessary to combine the speech big data analysis technology with the demand analysis of the tourism industry. The purpose of this paper is to study the effective model of tourism consumer demand prediction using big data analysis. In the process of completing this paper, we consulted a large number of research results of big data analysis technology, tourism-related books, and demand prediction models in HowNet, VIP, and other network databases as well as campus libraries, summarized the related concepts of tourism, and used big data analysis technology to predict the demand of tourism consumers. Understand the needs of tourism consumers on major tourism websites, and extract the indicators that will affect the forecast results of consumer demand, establish a demand forecast model based on the indicators, and analyze its forecast effects through comparative analysis to understand its advantages and disadvantages, in order to establish a tourism demand forecast models providing actionable advice. Through the practical application case of the demand forecasting model, this paper puts forward the development strategy of tourism. The experimental results show that the mean square error of the neural network model is less than 2.5, which is more suitable for predicting the number of tourists, indicating that different models are suitable for predicting different indicators. The main contribution of this research lies in the modeling and analysis of regional tourism characteristics and tourists' willingness, so as to achieve accurate prediction of tourists in scenic spots and formulate targeted plans. [ABSTRACT FROM AUTHOR]

In recent years, the concept of the Internet of Everything has gradually entered all walks of life, and the combination of big data technology and the Internet of Everything applied to other industries has become the current trend of industrial innovation. The integration between the tourism industry and the cultural industry is an inevitable trend for the sustainable development of the tourism industry. New science and technology are constantly innovating the way of life of human beings. If the tourism industry wants to maintain a state of rapid development, it must focus on the innovative development and digital construction of cultural tourism, develop new cultural tourism products, and propose new marketing innovation models. This promotes faster and better development of the tourism industry. Based on this, this paper designs a questionnaire and uses statistical methods to analyze the data obtained by the questionnaire, summarizes the current problems in the innovation and digital construction of the cultural tourism industry, and proposes solutions in a targeted manner, in order to improve the cultural tourism industry. Healthy development provides incremental contributions. [ABSTRACT FROM AUTHOR]

The urbanization process is speeding up in the Internet age, and many architectural heritages will be harmed as a result. This paper examines the coupling relationship between architectural heritage protection and tourism development using the concepts of architectural heritage protection and tourism development, as well as coupling theory. A preliminary analysis of the mechanism of the coupling relationship between architectural heritage protection and tourism development is conducted. Under unsupervised conditions, the classification results and quantitative index classification results of architectural heritage are obtained using the cluster analysis method. The sigs of historical, artistic, scientific, and humanistic factors, according to variance analysis, are 0.003, 0.041, 0.011, and 0.013, respectively, all of which are less than 0.05, with a significant difference. The spatial factor variable has a sig value of 0.52, which is not significant. This method can be used to not only classify heritage but also to quantify architectural heritage factors. In the future work of cultural relics protection, we can broaden the scope of protection measures and make recommendations to protection workers. [ABSTRACT FROM AUTHOR]

At present, China's tourism market is huge, and traditional hotel accommodation is not popular with young people, and homestays are just in line with young people's yearning. And most of the current push methods are determined by consumption on large platforms such as Ctrip, which not conforms to the recommendation method of homestays, so it aims at personalized marketing research for homestays. In this paper, the multi-information fusion sensor network is used to push B&B hotels in tourist areas to achieve B&B hotel marketing and maximize profits. Using the multi-information fusion sensor network to carry out personalized platform design, platform users can easily and quickly search for the homestay they want and can realize the automatic push function to achieve the rationality of marketing. The experimental results in this paper found that the average relative speedup increased from 1.9 to 17.7 for the execution of the scoring-oriented algorithm in the algorithm compared to a single computer. Performing the sort-oriented algorithm in the algorithm, the average relative speedup increased from 1.9 to 18.8. It can illustrate the effectiveness of the design system in this paper, and the software can carry out personalized marketing for homestays. [ABSTRACT FROM AUTHOR]

With the development of economy, people's demand for travel is also increasing. They are no longer satisfied with the single standardized travel products of travel agencies and begin to pursue personalized. When many customers travel, the most important thing is their subjective feelings. This is also the focus of tourism enterprise research. Therefore, introducing experiential marketing into tourism and realizing experiential marketing can enhance the sense of experience of tourism, improve the economic benefits of tourism, and promote the development of tourism. This paper mainly studies the application of experiential marketing in tourism industry. In this paper, based on the tourism situation of Qingyan Ancient Town and the tourism marketing activities of other tourist attractions, combined with my own design, we developed an optimized design scheme of experiential marketing. In the process of implementation, there are still areas to be improved, and the author will continue to study and learn from the sharing of other scholars. The purpose of this paper is to study the validity of marketing strategies of tourist attractions based on experiential marketing. On the one hand, the research of this paper can enrich the theory of experience marketing; on the other hand, it can also provide reference for the reality of tourism marketing, which has theoretical and practical significance. [ABSTRACT FROM AUTHOR]

There exist various challenges introduced by a large number of multimedia photos and videos for personalized travel recommendation in the era of big data. In order to resolve such challenges, a context-aware personalized travel recommendation system based on data mining is proposed in this study. It is a framework that can locate and summarize travel locations based on a user-given collection of geotagged photos and build up each user's travel history to obtain their travel preferences, so as to perform contextual multiattribute personalized queries, thereby recommending travel locations that best suit their interests. The primary objective is to lay the foundation for developing personalized travel solutions and help the transformation and upgrading of the tourism industry. Thus, this paper proposes a design and application of a multiattribute travel information recommendation model based on user interests for the contradiction between the personalized travel demand of tourists and traditional travel methods. It analyzes the designed travel transportation network and builds a prototype system for travel recommendation by mining a large number of scenic spot information datasets. In association to this, an advanced recommendation algorithm is also designed. The experimental results reveal the fact that by integrating various attributes, the comprehensive evaluation mechanism of scenic spots is capable of providing enhanced reasonable and comprehensive evaluation of scenic spots, which lays the foundation for subsequent route recommendation. Secondly, in comparison to the existing path recommendation algorithms, the recommendation algorithm proposed in this paper has the potential to meet various constraints and goals of the users and recommend routes that have better reasonableness and diversity. Also, this algorithm has low complexity in terms of running time which acts as an added advantage. [ABSTRACT FROM AUTHOR]

In order to study the intelligent tourism route planning scheme based on a weighted mining algorithm, firstly, a traveling salesman problem based on an improved weighted mining algorithm is proposed. Then, based on the traveling salesman problem, the constraints of travel time and driving time are added, and the mathematical model aiming at the minimum number of travel days is established. The improved weighted mining algorithm is used to solve the model: firstly, the scenic spots are divided into regions according to provinces and solved; then, the regions of some scenic spots are modified and solved again according to the solution results, and the specific itinerary is given; finally, based on the route planning problem and considering a variety of transportation modes, a double objective mathematical model with the optimal tourism cost and the best tourism experience is established. When solving, the double objective is reasonably transformed into a single objective, the model is solved by comprehensively considering the tourism cost and tourism experience, and the specific schedule is given according to the solution results. [ABSTRACT FROM AUTHOR]

This paper presents an in-depth study of a statistical system using image feature extraction technology for cultural heritage tourism information. Based on the specificity of the tourism industry, it combines the technical architecture, framework of image feature extraction, GIS basic design, database design, and other related theories; realizes visual mapping through relevant tourism statistics in GIS; and uses the spatial analysis capability of ArcGIS software to complete the statistical scheme of cultural heritage tourism information data in the context of Internet big data. In this paper, the image preprocessing process is accelerated by the parallel optimization of the filtering operation and down-sampling operation in the relevant image preprocessing algorithm. The preprocessed images can effectively improve the recognition effect of the image feature extraction and classification stage. Based on cultural heritage tourism information mining results, the design and development of the tourism information statistics platform are carried out. The platform is built and developed using GIS secondary development technology and HTML + CSS front-end development technology API, Echarts, and other image feature components. The platform enables the massive and disorganized tourism text information to be displayed in a way that is easier to understand in both maps and text. This paper's research on critical technologies provides technical support for image feature extraction of tourism statistics, which has specific practical value. Implementing the cultural heritage tourism platform offers a visual operating environment for tourism, enabling cultural heritage tourism businesses to tap into user preferences so that users can effectively access the travel options that interest them, which is conducive to promoting the development of travel recommendation platforms. [ABSTRACT FROM AUTHOR]

With the rapid development of image video and tourism economy, tourism economic data are gradually becoming big data. Therefore, how to schedule between data has become a hot topic. This paper first summarizes the research results on image video, cloud computing, tourism economy, and data scheduling algorithms. Secondly, the origin, structure, development, and service types of cloud computing are expounded in detail. And in order to solve the problem of tourism economic data scheduling, this paper regards the completion time and cross-node transmission delay as the constraints of tourism economic data scheduling. The constraint model of data scheduling is established, the fitness function is improved on the basis of an artificial immune algorithm combined with the constraint model, and the directional recombination of excellent antibodies is carried out by using the advantages of gene recombination so as to obtain the optimal solution to the problem more appropriately. When the resource node scale is 100, the response time of EDSA is 107.92 seconds. [ABSTRACT FROM AUTHOR]

So far, there are few reports on the evaluation and analysis of sports tourism resources in Haikou by the combination of qualitative evaluation and quantitative evaluation. This paper comprehensively uses Delphi method, questionnaire survey method, analytic hierarchy process, mathematical statistics, and other research methods to take Haikou sports tourism resources as the research object. Through the field investigation of Haikou sports tourism resources, this paper comprehensively evaluates Haikou sports tourism resources by combining qualitative and quantitative evaluation methods, in order to improve and innovate the theoretical level of Haikou sports tourism resource evaluation and provide data support and practical basis for the development and utilization of sports tourism resources in Haikou, so as to promote the sustainable and efficient development of sports tourism industry in Haikou. The evaluation results of sports tourism resources in Haikou are divided into four parts: advantages, disadvantages, opportunities, and challenges. It is considered that the advantage of sports tourism resources in Haikou is the scarcity of resources, unique charm, exciting events, and strong appreciation. The disadvantage is that the popularity of sports tourism resources in Haikou is low, and the suitable travel period is short. The opportunity is that the natural environmental conditions and the traditional sports characteristics of ethnic minorities are prominent, the cooperation with surrounding cities in sports tourism is deepened, and the tourist market potential is huge. The threat is that the market competition of homogeneous sports tourism products is fierce, the conditions of sports tourism human resources are poor, the publicity methods are old, and the development of local air transportation is backward. [ABSTRACT FROM AUTHOR]

With the win-win development of tourism and the Internet, word-of-mouth ranking of tourist attractions is a valuable reference factor. We try to find a correlation between tourist reviews and taste ranking of tourist attractions. We study the sentiment features of tourist online reviews from the technical perspective of natural language processing, so we propose an improved long short-term memory (LSTM) framework for sentiment feature extraction of travel reviews. We abandon traditional dictionaries and machine learning methods. A deep neural network approach was chosen to decompose multisentiment travel reviews into different morpheme levels for classification. Then, through preprocessing, text sentiment topic detection, and sentiment classification network, an accurate grasp of the sentiment features of reviews is finally achieved. To test the performance of our method, we built a web review database by crawler for experimental validation. Experimental results show that our method maintains more than 90% accuracy in comment sentiment detection, significantly outperforming dictionary methods and machine learning methods. [ABSTRACT FROM AUTHOR]

The vigorous development of tourism has made rural tourism a highlight of the new era. In order to better realize the classification of rural tourism features, this paper proposes a knowledge recognition algorithm based on hierarchical clustering analysis. Firstly, the rationality of the optimization of the rural tourism feature algorithm is analyzed in this paper; secondly, the rural tourism feature classification index system is constructed based on the hierarchical clustering analysis; finally, the index weights are clearly divided according to the characteristics through the hierarchical clustering analysis of the knowledge identification algorithm. According to the specialties of hierarchical clustering analysis, the criteria of the algorithm are determined, and the characteristics of rural tourism are carefully classified. Rural tourists can visit different scenic spots in high density and improve the mobility of rural tourism. The experimental results show that: through the analysis of the characteristic classification data of the Rural Tourism College in this paper, it can be seen that the average daily income of the scenic spot is higher than that of the traditional rural scenic spot. The average daily income of rural tourism has increased by more than 261,900 yuan, which has largely promoted the development trend of rural tourism in my country. It is proved that the hierarchical clustering analysis method is helpful for rational zoning and serious thinking about the characteristics of rural tourism. This paper provides a reference for promoting the classification of rural tourism. [ABSTRACT FROM AUTHOR]

New media is a scientific and technological product under the background of the new era, and now new media technology has been widely used in all aspects of social development, and it has spawned the arrival of the new media era. In the new media environment, the survival and development environment faced by enterprises have undergone earth-shaking changes; if you still follow the traditional marketing methods, it will not only reduce the effectiveness of marketing but also because of the deviation from the development requirements of the times be eliminated, bringing threats to the survival and development of enterprises. The development of new media, represented by the Internet and mobile phones, has brought great changes to various industries in the world including tourism. The market size of the travel network is expanding, and online sales are soaring. At the same time, all kinds of tourism organizations often have blind optimism in the construction of tourism networks and lack of reasonable application means, so that tourism network marketing is far from exerting its potential in reality; on the one hand, it cannot provide transaction services that satisfy both supply and demand; on the other hand, it also affects the economic benefits of tourism organizations. Compared with the rapid development of the tourism media industry, the theoretical research related to the actual situation lags significantly. Although the number of related studies at home and abroad is increasing, most of them stay at the level of explanation and introduction, and the analysis of the tourism industry chain is not enough. The breadth and depth of the study are sorely lacking. The future letter needs to summarize the successes and failures of the development of the tourism media industry from a theoretical height and carry out theoretical improvement to guide practice, and it also needs to strengthen forward-looking research, summarize the laws and models, and put forward practical implementation plans to improve the overall development level of the tourism and media industry. Aiming at the problem of new media marketing strategy in shopping tourism scenic spots, this paper applies descriptive research, questionnaire survey, statistical processing, mathematical model, and other research methods to analyze the characteristics of new media such as prominence of personalization, increased audience selectivity, and diversified forms of expression presented by new media compared with traditional media, as well as the differences between the two in terms of communication status, dominant state, and audience state. 4I Marketing theory is applied to new media marketing, the changes in the marketing environment and marketing needs under the background of new media, and the extensive impact of these changes on the marketing mix strategy; the basic strategies and expected effects of new media marketing of tourism enterprises are studied from the aspects of product, price, channel, and promotion; the IPA model is constructed, and the empirical analysis of the shopping tourist attractions of Changshu Garment City in China is carried out. Through the research of this paper, in theory, the understanding of new media marketing is enriched, and in fact, it provides guidance for optimizing the new media marketing strategy of tourist attractions. [ABSTRACT FROM AUTHOR]

In view of the problems of the traditional cluster analysis algorithm such as strong dependence on the initial cluster center, the traditional k-means cluster analysis algorithm is improved and the experiment proves that the improved algorithm has a better clustering effect; in view of the problems of the traditional tourism route planning, the improved k-means cluster analysis algorithm is applied to the intelligent tourism route planning scheme design and an intelligent tourism planning scheme based on the cluster analysis algorithm is proposed; the tourists' preference metric is fully considered, and the experimental results show that the scheme has certain reasonableness and reference value. [ABSTRACT FROM AUTHOR]

With the development of tourism, rural tourism as an industry with great development potential is gradually attracting urban consumers. Considering the differences between different types of rural tourism, I refine it at the visitor level to balance the differences in visitor groups of different types of rural tourism. I propose an improved LSTM framework for rural tourism theme feature extraction. I chose a deep neural network approach to decompose the diverse rural tourism word of mouth into different tourism themes. Then, through tourist feedback data preprocessing, destination theme detection, rural tourism type classification, and word-of-mouth management prediction network, I finally achieve an accurate grasp of rural tourism word-of-mouth features and develop corresponding tourism management strategies. To test the performance of our method, I established different types of rural tourism databases through field surveys for experimental validation. The experimental results show that our method achieves over 90% accuracy in review sentiment detection. [ABSTRACT FROM AUTHOR]

YouTube is one of the emerging technologies that impact consumer behavior. In the tourism sector, travel video blogs (travel vlogs) have become increasingly popular since they are highly influential in influencing travel behavior. Similarly, YouTube travel vlogs are highly engaging and provide consumers with destination experiences. This study determines how consumer behavior expressed through emerging technologies, such as travel vlogs on YouTube, can influence travel intention and electronic-Word of Mouth (e-WOM) behavior. By incorporating the use and gratification (U&G) perspective, this study examines the role of information seeking and entertainment as motivational factors for watching travel vlogs as a source of satisfaction. It will, in turn, lead to a greater degree of engagement. The current study investigated the emotional and presence aspects of travel videos to determine engagement. To evaluate the research hypothesis, we collected data from 300 respondents who had traveled to a destination after watching a travel vlog. We tested the hypothesis by utilizing structural equation modeling, a feature of the Smart-PLS 3.0 software. The results indicate that consumer motivations for watching travel vlogs, including the desire for information and entertainment, significantly impact customer engagement, both emotion and presence. Customers who are emotionally engaged and perceive travel vlogs as having a high presence will significantly affect their travel intention behavior and influence them to share e-WOM. In addition, the current study provides insight into tourism managers and academics on how travel vlogs as emerging technologies influence consumer behavior. [ABSTRACT FROM AUTHOR]

With the rise of the sports tourism industry, the group of sports tourism consumers is also growing. The study of the behavior of sports tourism consumers has therefore become a popular topic. This paper firstly organizes and analyzes the research results of scholars, finds out the deficiencies, and finds out the methods suitable for this research. On this basis, this research is practiced, and the methods found are successfully improved to add personal innovation points. Then, the definition and characteristics of cloud computing and mobile big data are elaborated. Next, this paper selects some consumers and their consumption behaviors participating in X city's sports tourism town, golf sports tourism, skiing sports tourism, and outdoor sports tourism projects as the research objects and conducts investigation and research by means of questionnaires and interviews. At the same time, the managers and operators of some sports tourism scenic spots are selected as the research objects, and the investigation and research are carried out through the interview method, which provides a realistic basis for the study of consumer behavior. According to the survey, the proportion of sports tourism consumers who choose to travel in summer is as high as 90.10%. [ABSTRACT FROM AUTHOR]

With the development of information technology around the world, the research on the integration of data mining and data fusion is becoming more and more in-depth. In recent years, sports tourism has developed better and better, with the growing demand for tourism, and the tourism industry has developed by leaps and bounds. Tourism is a new economic growth point in many regions. But it also faces many challenges from both internal and external sources. The tourism destination system is an extremely important subsystem in the tourism system. The quality of its spatial structure not only affects the quality of tourism activities, but also affects the effective operation of the tourism function of the destination system. This paper studies the spatial characteristics of sports tourism destination system based on data fusion and data mining technology. The external environment of sports tourism development, the distribution of sports tourism resources, and the spatial structure of sports tourism are deeply studied by using Gini coefficient, nearest neighbor index, and other technical methods. A questionnaire was also designed to study the physical activity preferences of different age groups. The results show that the nearest neighbor index R of fitness sports tourism destinations is less than 1, which is in a clustered distribution state. The main group of sports tourism activities is mainly concentrated in the age group of 16–45. [ABSTRACT FROM AUTHOR]

The cultural tourism industry combines the similarities of the cultural industry and tourism industry, which can be the most effective way to meet people's spiritual and cultural needs as well as their leisure needs simultaneously, and has a vast development potential. However, there are numerous and dispersed areas where the market value of cultural tourism resources is clustered, and frequently, each city has the clustering area with the highest market value concentration of cultural tourism resources. This feature of the spatial distribution of the market value of cultural tourism resources is significant for promoting the development of cultural tourism as a whole and constructing the industry's overall structure. It has broad application potential for extracting and differentiating cultural tourism industry characteristics. Texture feature extraction is typically performed using dual-tree complex wavelet transform (DT-CWT) and Gabor wavelet. In this paper, we propose a multiscale DT-CWT and Gabor-based method for identifying the cultural tourism industry. The method first decomposes the images of cultural tourism into multiscale space using a Gaussian pyramid, then extracts the multiscale features of the images using DT-CWT and Gabor, and lastly achieves feature fusion. Using a support vector machine (SVM) classifier to achieve classification, the effectiveness of the feature extraction method is determined. The experimental findings demonstrate that the method proposed in this paper can achieve a high rate of recognition. [ABSTRACT FROM AUTHOR]

Rural smart tourism is a new tourism form supported by information technology, especially the Internet of Things technology. Smart tourism not only makes it easier for tourists to get diverse tourism information, but it also helps attractive sites improve their service capabilities. The optimization of the rural smart tourist service model, which is explored in this work, includes personalized recommendations of scenic sites. The main research contents of the paper are as follows: (1) A new algorithm for recommending tourism destinations with domain adaptability has been suggested. In the field of personalized recommendation of tourist attractions, most of the target domain data are unlabeled, and the model cannot be trained. When it comes to training, however, there exist source domain data sets that are completely labeled and can be used as a supplement to the target domain training data sets. (2) A personalized recommendation algorithm for tourist attractions with deep migration has been studied. Owing to the distribution difference between target data and source data, an adaptive layer is added to the convolutional neural network. The domain loss is then minimized to achieve deep feature transfer, and the model can then be trained to find scenic areas of interest to the user. Finally, the recommendation for tourist destinations with deep migration is done by examining the relationship between the target user and other users. [ABSTRACT FROM AUTHOR]

Liuzhou is a rich tourism city in China. It is well known for its ethnic and prehistoric culture, folk songs, rare stones, and urban landscape. The demand of tourism in Liuzhou city is increasing day by day. Therefore, a system is needed to accurately predict the increasing demand of tourism in Liuzhou city. For this reason, based on the examination of historical visitor data in natural sites especially in Liuzhou city, this research employs an important machine learning and deep learning approach. The purpose of this study is to identify the consumption patterns and improve prediction, prejudgment, and preparation abilities by incorporating them into an intelligent tourism service platform, preventing visitors from travelling at inconvenient times and providing appropriate suggestions to scenic site managers. In this paper, the Sparse Principal Component Analysis-Long-Short-Term Memory (SPCA-LSTM) model has been updated to the Sparse Principal Component Analysis Convolutional Neural Network Long-Short-Term Memory (SPCA-CNNLSTM) model to accurately predict the tourist traffic during the holidays. Convolutional and pooling layers are added to the network topology to extract the local characteristics of the input data. The data of passenger traffic and influencing factors of Liuzhou scenic area from the months of Sept. 2015 to Nov. 2019 were used as the data set for the experiments. A hybrid-forecasting model is also proposed in the paper, which first removes noise from international crude oil data using compression-aware denoising preprocessing and then combines compression-aware denoising with machine learning using artificial neural network (ANN) and support vector regression (SVR). The experimental result shows that the SPCA-CNNLSTM model predicts better values than the SPCA-LSTM model. [ABSTRACT FROM AUTHOR]

Folk culture village has beautiful village scenery and rich national culture connotation. With the accelerated development of folk culture village tourism industry, traditional villages become popular. In view of the problems existing in the development of folk culture village tourism in Yunnan Province, from the perspective of "Internet + cultural industry," this paper studies and analyzes the distribution status, tourism resources status and economic industry status of traditional folk villages in Yunnan Province, and uses grey correlation method to further analyze the relationship between culture, tourism industry and Internet industry. The results show that thanks to the construction of the Internet platform, the resources of Yunnan folk culture villages are known by more and more tourists. The number of tourists in Yunnan folk culture villages has increased from 1486.26 million to 3362.41, and the tourism income has also increased from 7.7 billion yuan to 21.697 billion yuan, reaching a sustained growth state. The results of the grey correlation analysis of folk culture village resources show that the correlation between cultural resources and various resources in Yunnan's folk culture villages is greater than 0.7, indicating that there is a strong correlation between cultural resources, Internet resources, tourism resources and industrial resources, with sufficient potential for development. In order to strengthen the sustainable development of folk culture villages, the convenience of the Internet and the integration of tourism resources can be used to create an "online + offline" tourism system to further increase the popularity of the villages. [ABSTRACT FROM AUTHOR]