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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]

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]

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]

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]

In the late twentieth century, with the rapid development of the Internet, e-commerce has emerged rapidly, which has changed the way people travel around the world. The greatest advantages of e-commerce are the flow of information and data and the importance of traveling freely to experience the mind and body in different fields. Tourism is an important part of the development of e-commerce, but the development of e-commerce tourism lags behind. To solve the current situation of the backward development of tourism e-commerce, this article studies the construction of a tourism e-commerce platform based on an artificial intelligence algorithm. By introducing modern information technology, based on a cloud computing platform, big data analysis, K-means, and other key technologies, this article solves the current situation of the development of an e-commerce platform. It also analyzes the construction methods of traditional cloud platforms and modern cloud platforms through comparative analysis and solves the construction methods suitable for artificial intelligence tourism. At the same time, combined with the actual situation of tourism, this article selects the appropriate networking method based on the analysis of the advantages and disadvantages of wired and wireless coverage methods and economics to complete the project design. Its purpose is to ensure that the work meets the specific construction needs and build an artificial intelligence-based smart tourism big data analysis model. It promotes the development of tourism e-commerce industry. It saves costs and improves efficiency for travel service providers. Then, according to the actual situation of tourism, it conducts demand analysis from the perspectives of tourists, scenic spots, service providers, tourism administrative agencies, etc. Experiments show that, through the practical application of the artificial intelligence tourism mobile e-commerce platform in this article, it can be seen that the artificial intelligence tourism mobile e-commerce platform designed in this article can meet the needs of customers for shopping-related tourism commodities. Tourists of attractions have increased by 3.54%, and the economy of tourist destinations has increased by 4.2%. [ABSTRACT FROM AUTHOR]

In order to improve the effect of modern tourism route planning, this article combines the cloud computing advancement algorithm to construct a tourism route intelligent planning system and combines the current people's tourism needs to carry out intelligent tourism route planning. Based on the fast forward algorithm of the program function equation, this article uses the fast forward algorithm to solve the discrete nonlinear program function equation and directly obtain the cloud computing data. In addition, the self-intersection phenomenon of the wave front must be considered when describing the characteristics of the cloud computing information field by using the process of the equivalent cloud computing information front expanding to the surrounding. Finally, this article constructs the intelligent system structure. The research results show that the intelligent travel route planning system based on cloud computing and marching algorithm proposed in this article can play an important role in modern smart tourism. [ABSTRACT FROM AUTHOR]

In rural tourism, precise visitor flow forecasts may aid management in making better decisions. It aids in the reduction of visitor crowds and trash. It also has the potential to improve visitor security. As a result, it is critical to continue to encourage tourism's long-term growth. However, regional tourist flow of rural tourism has the characteristics of high volatility, complex nonlinearity, and susceptibility to seasonal influences. Moreover, a single neural network model cannot learn both temporal and spatial correlation. Therefore, by examining the variables impacting regional tourist flow and integrating residual networks with fully linked networks, this research offers an enhanced Quad-ResNet model for forecasting regional tourist flow of rural tourism. To be specific, this model learns spatial correlation through deep convolution; combines four residual networks to learn temporal proximity, similarity, periodicity and tendency; and uses one fully connected network to learn seasonal effects. Furthermore, this study compares the Quad-ResNet model with LSTM, CNN, and ST-ResNet models on the same dataset for regional tourist flow prediction experiments. The findings reveal that the Quad-ResNet model has less error and is substantially simpler to train and predict than the LSTM model, making it more suited for predicting regional visitor flows in rural tourism. For relevant stakeholders, the generated model may serve as a useful decision-making tool. [ABSTRACT FROM AUTHOR]

In the traditional tourism teaching mode, the teaching activities between teachers and students are restricted by the teaching mode and teaching methods. This model fails to use efficient methods to improve students' learning ability, and students cannot communicate after class to improve their ability. Therefore, the adaptive virtual queue algorithm is introduced in the design of the remote tourism teaching and training system. During the design of the remote tourism teaching and training system, the adaptive virtual queue algorithm can be used to collect business data information with network technology and sensors and other equipment. The data information is stored in the constructed remote tourism teaching and training system. Finally, the experimental results show that the remote tourism teaching training system designed in this article can effectively improve students' learning efficiency by 95%, which effectively verifies that the training system can effectively improve the students' ability of independent learning and extracurricular communication through the use of this training system. [ABSTRACT FROM AUTHOR]

Smart tourism recommendation refers to supplementing suggestions for tourists' self-service sightseeing plans based on Internet technology, and recommending more effective and practical tourism information. This paper uses big data and artificial intelligence algorithms to study the smart tourism recommendation method in Southeast Asia. First of all, this paper gives a brief overview of the definition and classification of big data and artificial intelligence algorithms and then introduces tourism resources and smart tourism in Southeast Asia. Finally, this paper conducts a comparative experiment between the smart tourism development model based on big data and artificial intelligence algorithms and the traditional tourism development model and analyzes the utility of the development model from three aspects, namely, tourists' sense of experience and satisfaction, the scale of tourism transactions and the growth rate of tourism revenue, and the adequacy and harmony of tourism resource allocation. The final experimental results show that the overall average value of tourists' experience and satisfaction under the smart tourism recommendation mode based on big data and artificial intelligence algorithms is 88.84 points, which is 7.30 points higher than the traditional tourism mode, which verifies its effectiveness. [ABSTRACT FROM AUTHOR]

In recent years, the Internet of Things has developed rapidly in people's lives. This brand-new technology is flooding people's lives and widely used in many fields, such as medical field, science and technology field, and industry and agriculture field. As a modern technology, the Internet of Things has many characteristics of low power consumption and multifunction, and it also has the characteristics of data-aware computing. This is the characteristic of this new product. In people's daily life, the Internet of Things is also closely related to people's daily life. In the tourism industry, the Internet of Things can make the best use of everything and give full play to its various advantages as much as possible. The Internet of Things can perceive cross-modal tourism routes. So here, this paper summarizes various algorithms recommended by the Internet of Things for this tourist route and works out the experimental data methods of these algorithms for cross-modal tourism route recommendation. The proposed algorithm is verified by data simulation, compared with related algorithms. We analyze and summarize the simulation results. At present, there is no comparative analysis of the performance of ant colony algorithm, genetic algorithm, and its optimization algorithm in tourism route recommendation. On the basis of crawling the tourism data in the Internet, this paper applies ant colony algorithm, genetic algorithm, max–min optimization ant colony algorithm, and hybrid ant colony algorithm based on greedy solution to tourism route recommendation and evaluates and compares the algorithms from three aspects: average evaluation score, optimal evaluation score, and algorithm time. Experimental results show that the max–min optimization ant colony algorithm and the hybrid ant colony algorithm based on greedy solution can be effectively applied to automated tourist route recommendation. [ABSTRACT FROM AUTHOR]

The key technology of online travel recommendation system has been widely concerned by many Internet experts. This paper studies and designs a scenario aware service model in online travel planning system and proposes an online travel planning recommendation model which integrates collaborative filtering and clustering personalized recommendation algorithm. At the same time, the algorithm performance test method and model evaluation index are given. The results show that CTTCF algorithm can find more neighbor users than UCF algorithm, and the smaller the search space is, the more significant the advantage is. The number of neighbors is 5, 10, 15, 20, and 25, respectively, and the corresponding average absolute error values are about 0.815, 0.785, 0.765, 0.758, and 0.755, respectively. The scores of the six emotional travel itinerary recommendation schemes are all higher than 142 points. Only the two schemes have no obvious rendering effect. The proposed online travel itinerary planning scheme has potential value and important significance in the application of follow-up recommendation system. It solves the problem of low scene perception satisfaction in the key technologies of online tourism planning system. [ABSTRACT FROM AUTHOR]

Tourism industry can promote the prosperity of the local economy. In addition, the booming tourism business in recent years has also led to the development of related industries and services. According to high-tech technologies such as cloud computing, hybrid cloud, and panoramic image, we will plan to design a comprehensive and integrated architecture model of scenic spots that integrates all tourism information and various services, strive to create smart scenic spots, bring the best play experience to tourists, and strive to attract more attention and attention. The results show that (1) the overall average satisfactory approval of UI test is as high as 93%, which can meet the requirements. (2) After the improved panoramic image mosaic method, the correct rate is as high as 93.90%, the effect is 4.90% better than that before the improvement, and the algorithm is more efficient. (3) The scenic spot configuration management effect is excellent, and various situations in the scenic spot are effectively monitored. (4) The average response time per request of the original system is about 161 ms. Compared with the original system, the response time of the new system is reduced by about 57%, and the access success rate is 100%. The system in the experiment runs well and satisfies the integration of tourism information resources and services in scenic spots. [ABSTRACT FROM AUTHOR]

This paper researches and analyzes the evaluation of the competitiveness of ice and snow tourism, uses the improved fuzzy neural network algorithm to process the system flow diagram of ice and snow tourism development through the function and characteristics of the power system of ice and snow tourism, and finally selects more than 40 indicators of the three subsystems of resources, economy, and culture. Based on the construction of cloud fuzzy neural network model, the above method is used for experimental comparison analysis, and experiments are conducted through University of California Irvine (UCI) dataset and engineering examples to compare with the traditional cloud model, fuzzy neural network, and BP neural network to analyze the operation efficiency, accuracy rate, and several rules of the algorithm. Through the experimental comparative analysis, the cloud fuzzy neural network can fully take into account the randomness and fuzziness of the data, optimize the generation of cloud rules, avoid multidimensional rule disasters, and ensure the operational efficiency of the algorithm; the accuracy rate of the algorithm is improved relative to that of the traditional technology, and it applies to a variety of datasets. And the software is used to test the ice and snow tourism industry system dynamics model to realize the correctness and robustness testing of the model. After the constructed model can reflect the real situation within the error range, the final policy simulation of the model is carried out. [ABSTRACT FROM AUTHOR]

This article has been retracted by Hindawi following an investigation undertaken by the publisher [[1]]. We wish to credit our own Research Integrity and Research Publishing teams and anonymous and named external researchers and research integrity experts for contributing to this investigation. [Extracted from the article]