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Tourism culture and demand forecasting based on BP neural network mining algorithms

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Abstract

Under the background of large data, demand forecasting of rural tourism based on intelligent algorithm is a new direction to promote the development of rural tourism industry. This paper mainly studies the application of neural network intelligent algorithm in rural tourism. Firstly, from the perspective of inbound tourism demand, the influencing factors of inbound tourism demand are clarified. Considering the influence degree and quantification difficulty of each factor, seven influencing factors are extracted to construct the inbound tourism feature vector. Then taking Yangjiang inbound tourism as an example, we use the neural network model to forecast the number of inbound tourists in Yangjiang from 2018 to 2019. The mean square error of the network is 0.011695 and the coefficient R2 is 0.94744; the results of the model are acceptable. Finally, from the perspectives of changing marketing strategy and pricing strategy, this paper puts forward some suggestions for the improvement of rural tourism.

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Funding

This research is supported by the Fund for Shanxi “1331 Project” Key Innovative Research Team (No. 1331KIRT); project support of Guizhou Sports Bureau in 2018 (No. GZTY2018102); Program for the Philosophy and Social Sciences Research of Higher Learning Institutions of Shanxi (No. 201803051); and funding program of Shanxi Provincial Soft Science Research Project (No. 2017041022-3).

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  1. School of Physical Education, Shanxi University, Taiyuan, 030006, China

    Xiaofeng Shi

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  1. Xiaofeng Shi
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Correspondence to Xiaofeng Shi.

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Shi, X. Tourism culture and demand forecasting based on BP neural network mining algorithms. Pers Ubiquit Comput 24, 299–308 (2020). https://doi.org/10.1007/s00779-019-01325-x

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