The analysis of ecological security and tourist satisfaction of ice-and-snow tourism under deep learning and the Internet of Things

Baiju Zhang

doi:10.1038/s41598-024-61598-y

The analysis of ecological security and tourist satisfaction of ice-and-snow tourism under deep learning and the Internet of Things

Sci Rep. 2024 May 10;14(1):10705. doi: 10.1038/s41598-024-61598-y.

Author

Baiju Zhang¹

Affiliation

¹ The Tourism College of Changchun University, Jilin Northeast Asia Research Center On Leisure Economics, Jilin Province Research Center for Cultural Tourism Education and Enterprise Development, Changchun Industry Convergence Research Center of Culture and Tourism, Changchun Ice and Snow Industry Research Institute, Changchun, 130607, China. zbj@tccu.edu.cn.

Abstract

This paper aims to propose a prediction method based on Deep Learning (DL) and Internet of Things (IoT) technology, focusing on the ecological security and tourist satisfaction of Ice-and-Snow Tourism (IST) to solve practical problems in this field. Accurate predictions of ecological security and tourist satisfaction in IST have been achieved by collecting and analyzing environment and tourist behavior data and combining with DL models, such as convolutional and recurrent neural networks. The experimental results show that the proposed method has significant advantages in performance indicators, such as accuracy, F1 score, Mean Squared Error (MSE), and correlation coefficient. Compared to other similar methods, the method proposed improves accuracy by 3.2%, F1 score by 0.03, MSE by 0.006, and correlation coefficient by 0.06. These results emphasize the important role of combining DL with IoT technology in predicting ecological security and tourist satisfaction in IST.

Keywords: Deep learning; Ecological security; Ice-and-snow tourism; Internet of Things technology; Tourist satisfaction.