Suitability Evaluation and Analysis of the Human Settlement-Environment-Energy Coupling System Based on Information Entropy and Artificial Intelligence Algorithms

To address the issues of insufficient granularity and ambiguous identification of key driving factors in the evaluation of the Human Settlement-Environment-Energy (HSEE) coupling system, this study takes 30 Chinese provinces as research objects and constructs an interpretable coupling evaluation model based on "information entropy + artificial intelligence" using panel data from 2003 to 2023. Using classic AI algorithms (BP neural network, PCA, and SVM) combined with the entropy weight method, the model was constructed. The entropy weight method and PCA respectively calculated the system suitability scores, and the robustness was validated by the Spearman correlation test (r = 0.9392). Indicator importance was identified via BP neural network, SVM, and the Garson algorithm, and comprehensive weights were determined using the rank average method. The results show that: during the study period, the national average system suitability continuously increased with an average annual growth rate of 3.8%; eastern coastal provinces significantly outperformed western and northeastern regions; per capita water resources, per capita local fiscal revenue, and residential consumption level are the core driving factors; infrastructure indicators exhibit diminishing marginal returns; energy consumption and environmental protection indicators show nonlinear differentiation characteristics. This study integrates objective weighting and machine learning interpretability to provide a standardized methodological framework for evaluating the HSEE coupling system, offering data support for regional human settlement quality improvement and sustainable development policy making.