Research on Interpretable Tourism Demand Forecasting Based on VSN–xLSTM Model

To address the limitations of traditional tourism demand forecasting models in leveraging multi-source data and their lack of interpretability, this study proposes an integrated multi-data-driven interpretable forecasting framework incorporating historical visitor volumes, social media activities, holiday schedules, weather conditions, and seasonal indicators. This study develops a system-oriented tourism demand forecasting framework that integrates a Variable Selection Network (VSN) and an enhanced long short-term memory (xLSTM) architecture to jointly model and interpret multi-source demand drivers. The VSN module employs a dynamic feature weighting mechanism to automatically discern distribution characteristics and relevance variations across heterogeneous data sources, thereby assigning adaptive weights to input variables. The xLSTM model incorporates innovative exponential gating and matrix memory structures, enabling rapid adaptation to sudden tourist flow fluctuations while effectively capturing long-term cyclical dependencies. By combining VSN-derived feature importance weights with SHAP-based prediction attribution analysis, this framework offers dual-level interpretability—in both input feature selection and output explanation. Experimental results demonstrate that social media data significantly reflect tourist attention and travel intention and reveal distinctive demand-driving mechanisms for various types of tourism destinations. The study provides theoretical insights and empirical support for advancing tourism demand forecasting and management strategies.