Study on the statistical measure of tourism decision-making in the context of big data and artificial intelligence

A groundbreaking study demonstrates the feasibility of leveraging big data and artificial intelligence to optimize tourism decision-making, particularly for UNESCO World Heritage Sites. By employing ARIMA time series algorithms and CRITIC weight methods, researchers analyzed historical tourism data from Mount Emei Scenic Area, including Leshan Giant Buddha, to predict future trends with high accuracy. The study validated these predictions against actual values, showcasing how AI-driven analytics can efficiently allocate limited resources in the tourism sector. This approach offers actionable insights for stakeholders, enhancing resource management and strategic planning in heritage conservation and tourism development.

Chen, G., Wang, J., Huang, P., & Wang, H. (2025). Study on the statistical measure of tourism decision-making in the context of big data and artificial intelligence. Proceedings of the 2025 International Conference on Economic Management and Big Data Application, 8–12. https://doi.org/10.1145/3770177.3770179