Deep Learning-Driven Tourism Data Mining and Fuzzy Prediction Methods

Abstract

Tourism is a dynamic industry influenced by a variety of factors, including uncertainty about tourist behavior, seasonal variations, and the impact of external environmental factors. With the development of big data technology and artificial intelligence, the tourism industry has begun to seek more efficient ways to understand and predict tourists’ behavioral patterns and their impact on the tourism economy. However, traditional prediction models often struggle to accurately capture the ambiguity and uncertainty in tourism data, especially when confronted with complex time series data and multimodal data. This study proposes an innovative deep learning-driven tourism data mining framework that incorporates fuzzy logic to improve the accuracy of predictions regarding tourist numbers and expected revenue. By employing a deep learning architecture with multimodal fusion, the framework is able to handle both structured and unstructured data and utilize the attention mechanism to highlight key features. The integration of fuzzy logic further enhances the model’s ability to handle uncertainty and fuzzy information. The experimental results show that the proposed method performs better than the traditional baseline model under a variety of evaluation metrics, especially in dealing with seasonal variations and uncertainty. Specifically, in terms of tourist number prediction, the MSE, RMSE and MAE of the proposed method are 75.42, 8.68 and 5.23, respectively, while in terms of revenue prediction, these metrics are 1023.21, 31.99 and 23.42, respectively, which are significantly lower than the baseline models, such as
linear regression, support vector machine, random forests, and long and short-term memory networks. In addition, the proposed method shows high stability when dealing with seasonal variations in different months, which can provide reliable forecasting support for tourism companies throughout the year.

Keywords: deep learning, tourism data, data mining, fuzzy prediction

Cite As
1International Education School, Guangzhou College of Technology and Business, Guangzhou 510000, China
2School of Foreign Language and International Trade, Guangdong Polytechnic, Foshan 528000 China
3Macau University of Science and Technology, Macau 999078, China
4School of Tourism and Leisure Management, Fujian Business University, Fuzhou 350012, China
Xiaochen Li1, Jiayu Wu2, Yuqing Zhang 1,3,? and Yanling Xiao4

X. Li, J. Wu, Y. Zhang, Y. Xiao, "Deep Learning-Driven Tourism Data Mining and Fuzzy Prediction
Methods", Engineering Intelligent Systems, vol. 33 no. 6, pp. 645-655, 2025.