An Artificial Intelligence Prediction Approach for Behavioral Intentions of Health Tourism: a Protection Motivation Theory-based Perspective

Abstract

This study applies an artificial intelligence (AI) method, informed by the Protection Motivation Theory (PMT), to predict the behavioral intentions of tourists in a healthy town in Yunnan. This study looks at online search data to guess when a lot of tourists will come by combining text mining with the SPCA-LSTM model. This model combines seasonal and trend decomposition using Loess (STL) with Long Short-Term Memory (LSTM) networks. The model is more accurate than traditional forecasting methods and provides a daily average tourist flow estimate of 3,247 with minimal prediction errors. The average absolute error of 806.4074 and the root mean square error (RMSE) of 959.775 further highlight the model’s performance. This research contributes significantly to tourism management and strategic planning, particularly in health-related destinations. The model provides a reliable benchmark for predicting tourist flows enhancing decision-making processes in the tourism sector.

Keywords

• gray correlation
• text mining
• spca-lstm model
• protection motivation theory
• 68p30