Аннотация
With the rapid development of smart tourism, the effective analysis of massive user behavior data has become the key to improving tourism service quality. This paper proposes a visual analysis platform for smart tourism and leisure, which integrates multi-dimensional user behavior data mining, spatial-temporal trajectory analysis and interactive visualization technology. Through in-depth mining of user behavior data from 6 provinces and 52 tourist cities in China, the platform realizes functions such as tourist flow pattern recognition, hotspot attraction prediction and personalized recommendation. Experimental results show that the proposed trajectory clustering algorithm achieves an accuracy of 87.342% in predicting scenic spot dwell time, a hotspot area recognition precision of 91.567%, and an F1 value of 0.883 for user preference prediction. The platform has been deployed in 5 pilot cities, providing real-time decision support for tourism management departments, effectively improving tourist satisfaction by 15.7% and reducing scenic area congestion by 23.4%. This research provides an effective technical solution and practical reference for smart tourism big data analysis.