Spatiotemporal dynamics and forecasting of public attention to entrepreneurship education: An entropy-based modeling approach

Abstract

Entrepreneurship education is increasingly important with the development of digital economy and post-pandemic. However, the distribution of public attention to entrepreneurship education (PAEE) remains unclear. Based on Baidu Index data from 2016 to 2024, this study first explores its spatiotemporal patterns. Then, an analytical framework based on Shannon and Tsallis entropy is established to analyze the spatial patterns, and a multi-model forecasting system combining SARIMA, LSTM, XGBoost, and other models is developed. The results show that PAEE shows a trend of continuous decrease, and the concentration in eastern provinces is stronger. Overall, hybrid models generally outperform single models, while the entropy-weighted ensemble demonstrates competitive performance by enhancing robustness and stability. The results of this study can provide quantitative reference for improving policies and promoting fair allocation of regional resources in entrepreneurship education. Meanwhile, this study offers a replicable framework to analyze patterned social behavior and forecasting trends in other fields.