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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 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]

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]

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]

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]

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]

In contemporary China, tourism is being upgraded to a modern service industry and strategic pillar industry. With the rapid development of modern information and communication technology and the continuous refinement of tourism market demand, tourism has entered a new era of smart tourism. Information runs through the whole process of tourism activities and is the key to determine the quality of tourism. Tourism public service is a very important basic support for tourism activities. It is of great significance to meet the diversified needs of tourists and optimize various services and government management functions. Based on the analysis of the connotation of tourism supply chain performance evaluation, this paper constructs a tourism supply chain performance evaluation index system and performance evaluation model suitable for analytic hierarchy process and studies the product price, production capacity, credit, complaint rate, and management level of its upstream tourism supply enterprises. To test the effectiveness of AHP on tourism supply chain performance evaluation, the comprehensive evaluation results and relevant suggestions for improving performance are obtained, which also proves the extensibility of the performance evaluation index system and provides an effective method for improving the performance of each link of China's tourism industry and tourism service quality. [ABSTRACT FROM AUTHOR]

In recent years, the domestic self-help tourism market has developed rapidly, so it is necessary to develop a tourism guidance system based on the Internet of Things. The brilliant smart tourism has emerged under this background. This is a low-carbon smart operation model that integrates technology and management, that is, using big data technology as the basis to make traditional rural tourism and digitization more intelligent. Smart tourism can play a very good role in improving the management of rural tourism, developing rural economy, and improving tourism management and services, thereby promoting the development of the tourism industry. The research is aimed at the construction and development of the rural smart tourism strategic model based on big data technology. Through the actual investigation of rural smart tourism combined with related literature, the innovative application of big data is described and analyzed, and the real-time analysis of tourist attractions is carried out through big data. The density of people flow helps tourists choose routes according to the density, and manages the dense flow in time, thus effectively preventing the series of risks caused by the excessive flow of people and strengthening the management of tourists. It also puts forward the countermeasures that the development of rural smart tourism needs to improve the rural smart tourism information platform, formulate smart service standards, create a smart talent service platform, and create a new model of rural smart tourism marketing. [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]

This research focuses on the analytic hierarchy model in the decision-making system that has a more complex structure and maintains the stability of the system, models the application process with the complexity and diversity of the rural economy, collects sample data with the help of different types of rural tourism questionnaire surveys, and integrates the data of rural tourism and other tourism into the model. The following are obtained: (1) During the level analysis, each phenotype track uses RRM, C 1 = 0.26 , C 2 = 0.223 , C 3 = 0.52 , C 4 = 0.25 , C 5 = 0.833 , C 6 = 0.442 , C 7 = 0.75 , C 8 = 0.127 , C 9 = 0.876 , C 10 = 0.792 , C 11 = 0.049 , C 12 = 0.16 , C 13 = 0.166 , and C 14 = 0.049. The problems of the complex structure of the evaluation can be divided into simple analysis modules, and each module is analyzed at a level. The phenotypic trajectory of each individual is divided into target layer, standard layer, and scheme layer. (2) Arrangement and decision modeling were performed according to one or several indicators of different factors. In the hierarchical random regression model, APC = 0.214 , UPUA = 0.042 , TO = 0.081 , YPUA = 0.082 , PCP = 0.068 , and APS = 0.067. The characteristic quantity analysis of different environments can be carried out, and the amplitude error and frequency error obtained are relatively small. IAND = 0.115 , AVA = 0.198 , RD = 0.119 , PI = 0.041 , PCCL = 0.142 , IOC = 0.201 , and DSTC = 0.069. The comparison shows that the hierarchical analysis model is better than the hierarchical random regression model. (3) High-efficiency hybrid model correlation acceleration is the worst model. The experimental data are APC = 0.147 , UPUA = 0.029 , TO = 0.055 , YPUA = 0.06 , PCP = 0.047 , APS = 0.046 , IAND = 0.079 , AVA = 0.136 , RD = 0.082 , PI = 0.028 , PCCL = 0.098 , IOC = 0.139 , and DSTC = 0.048. (4) The predicted 2020 data and the actual data have small errors. The data obtained by the AHP model is GDP = 1262.1 , finance = 185.09 , budget = 68 , tax = 51.92 , fund budget = 69.23 , transfer income = 40.14 , debt income = 7.73 , disposable financial power = 177.37 , fiscal expenditure = 191.26 , public budget = 88.68 , government expenditure = 71.39 , transfer expenditure = 23.46 , debt expenditure = 7.73 , and last year balance = 2.39. [ABSTRACT FROM AUTHOR]

With the advent of the era of mass tourism, rural tourism has received increasing attention from tourism-related personnel, such as the wide application of Internet, cloud computing, and big data technology, making it possible to build a development path for rural tourism based on the background of big data. The purpose of this paper is to study the development path and data mining mode analysis of rural tourism under the background of data and propose a clustering integration algorithm. The article gives an overview of the fit relationship between rural tourism and big data. This paper takes the farm tourism data in area A as the research object and through the data analysis of the rural tourism development path using the clustering integration algorithm; it explores the effectiveness of the clustering integration algorithm in the construction of the rural tourism development path. The experimental results show that compared with the traditional rural tourism development model in 2020, the tourism revenue in 2021 will increase by more than 300,000 yuan. [ABSTRACT FROM AUTHOR]

Rural tourism (RT) and cloud computing are important forces in implementing the rural revitalization strategy, and they play a key role in accelerating agriculture and rural area modernization, urban-rural integration, poverty alleviation in impoverished areas, and so on. RT's development is influenced by a variety of factors due to its high industrial relevance and strong driving function. The development of RT is examined from the perspectives of data mining and cloud computing in this paper, and a data mining model of the RT development's driving mechanism is built. Five driving modes of RT development are simulated by adjusting the parameters of industrial economy, resources, environment, and social culture in H city as an example. The simulation results are compared and analyzed in various modes. Finally, the coordinated development model of industrial economy, social culture, resources, and environment is found to be the best model of RT development in H city, and countermeasures and suggestions for RT development in H city are proposed based on the analysis results. [ABSTRACT FROM AUTHOR]

With the rapid development of information technology, people's acquisition of tourism and other information is increasingly dependent on images and other information. Aiming at the low efficiency of traditional image retrieval methods in processing massive image data, an image retrieval method based on wireless communication network is proposed. Based on the salient area and wireless communication network combined with the hash method to extract local CNN features, simulate INRIA Holidays Data set and Oxford Buildings Data set, and calculate the accuracy and recall rate of search results based on the returned results of tourist attractions pictures. This article designs an experiment to verify the accuracy and recall rate of search results. By comparing the feature hash function to generate the hash code and the Hamming distance between each hash code in the image library, the image is queried, and the final search result is obtained: The more searches, the lower the accuracy and recall rate. This also proves to a certain extent that the CNN feature extraction technology can be used for travel image search, improving the search accuracy by 20%.The wireless communication network is still of great significance to the future social development. It is necessary to conduct in-depth research, not only the image retrieval of tourist attractions proposed in this article but also the potential value of wireless communication networks from multiple angles and more comprehensively. [ABSTRACT FROM AUTHOR]

In recent years, due to the rapid development of rural tourism, rural tourism has lost its unique rurality, which has led to a certain impact on the sustainable development of rural tourism. Primarily, based on the rural characteristics, the social environment development, population development, and economic development are taken as the research indexes, and the evaluation index system of rural tourism destination is constructed. Afterward, an empirical study on the spatial pattern of rural tourism is carried out with examples, and the model is simulated and analyzed by MATLAB software. Finally, the spatial autocorrelation method is used to analyze the evolution characteristics of the rural tourism spatial pattern. The results show that through the analysis of the evaluation error curve of the Back Propagation Neural Network (BPNN), the evaluation error and the actual error range are within 0.08%, which proves that the BPNN algorithm has good calculation accuracy. The BPNN rural tourism destination rurality evaluation model established here can make an effective evaluation of rural tourism space. The results show that the proportion of employees in the primary industry and the penetration rate of mobile phones are the decisive factors in the adjustment of industrial structure and social environmental factors, respectively. Rural per capita tourism income and the proportion of primary industry output value will also have a certain impact on rural evolution. Certain guiding significance is provided for the sustainable development of rural tourism. [ABSTRACT FROM AUTHOR]

Whether the characteristics of rural tourism changes or not provides the scale and basis for judging whether the rural tourism landscape has changed, but it cannot provide a judgment on the impact of rural tourism landscape changes. The impact is relative to the rural tourism landscape goal. The determination of rural tourism landscape objectives provides a baseline for judging the direction and impact of rural tourism characteristics and provides a prerequisite for rural tourism landscape actions. The determination of the quality target of the rural tourism landscape is mainly determined by the internal process and external demand of the rural tourism landscape. Through in-depth research on the frequent pattern growth algorithm FP-Growth, the algorithm can find frequent item sets by not generating candidate item sets. The core of the algorithm is the frequent pattern tree FP-tree, which can efficiently compress the transaction database. Based on the advantages of FP-tree, this paper improves a FP_Apriori algorithm based on frequent pattern trees. This algorithm projects the entire transaction database onto the FP-tree, avoiding a lot of I/O overhead. At the same time, I propose a more directional and targeted search strategy for FP-tree, which reduces the running time of the algorithm and uses the principle of the Mapping_Apriori algorithm to prethin the frequent item sets. This article uses the text analysis method of network data to excavate the characteristics and internal structure of rural tourism demand. The rural tourism market has a wide range of needs and multiple levels, and traditional research methods such as questionnaires have limited sample size and sample structure. With the help of network data, text mining, and other statistical analysis methods, in-depth empirical research on the characteristics and spatial structure of rural tourism in a certain region can cover more research groups. The research confirms that the results of using text analysis and questionnaire analysis on the perception of destination image are relatively consistent. Therefore, the network text analysis method is an effective tool to study the demand structure of the rural tourism market. [ABSTRACT FROM AUTHOR]

After years of vigorous development, the scale of rural tourism industry has gradually expanded throughout the country, and it has become an important means of coordinating urban and rural areas to drive farmers' incomes and get rich. Nowadays, the level of informatization is constantly improving, the construction of smart cities is constantly advancing, the personalized demand of tourists is increasingly strong, especially the rural tourist spots scattered in the vast countryside which have a great spatial breadth, and the traditional management method can no longer meet the needs of the source objects whose main travel mode is self-driving. Therefore, only when rural tourism develops in the direction of intelligence can it successfully realize the transformation and upgrading of rural tourism industry. This paper takes a rural tourism resort in a certain area as the research object and builds an intelligent rural tourism environmental suitability evaluation system with three criterion levels: natural geographic conditions, traffic environmental conditions, and tourism spatial conditions. The weight of each indicator layer is obtained through the analytic hierarchy process, and the reclassified raster data of indicators is weighted and superimposed on the ArcGIS operating platform to obtain the evaluation result of the environmental suitability of smart rural tourism. According to the results of suitability evaluation, the most suitable area is selected as the potential area of the intelligent rural tourism environment in a certain area. According to the natural and cultural tourism resource conditions in the block, the potential block is divided into functions. A comprehensive comparison of the suitability spatial differentiation map of the smart rural tourism environment in a certain area with the suitability spatial differentiation map of each subsystem can more clearly find that the suitability score of the economic development subsystem and the basic support subsystem is a county-level village in a certain area. Among them, the suitability of the rural economic development subsystem is similar to the distribution of the suitability of the smart rural tourism environment in a certain area; that is, it is positively correlated with the suitability of the smart rural tourism environment. Therefore, improving the level of rural economic development and increasing investment in the construction of a smart rural tourism environment play an important role in improving the level of public services and infrastructure coverage. [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]