A Hybrid Fuzzy–Bayesian Modeling Method for AI-Driven ESG Governance and Corporate Strategic Decision Optimization

Environmental, social, and governance (ESG) performance has become a key dimension for assessing corporate sustainable competitiveness and long-term value, yet existing methods have difficulty simultaneously addressing multi-source heterogeneous information, fuzzy assessments, intertemporal evolution, and multi-constraint optimization. To address these issues, this study proposes a distributed dynamic hybrid fuzzy–Bayesian decision model for AI-driven ESG governance. The model comprises six modules—AI capability, action mechanisms, environmental performance, social performance, governance performance, and strategic performance. Fuzzy soft evidence and belief rules are used to integrate continuous, discrete, and linguistic information; a dual-population cooperative genetic algorithm learns the network structure under theoretical constraints; and dynamic inference, risk-contribution diagnosis, and simulated annealing are combined to optimize corporate strategies. Under the tested 2012-2024 Chinese A-share sample and the designed simulation scenarios, the model achieved an Accuracy of 0.853, a Macro-F1 of 0.838, and an AUC of 0.912, while reducing Log Loss and the Brier Score to 0.441 and 0.108, respectively. Under the model-optimized scenario, ESG risk decreased from 0.346 to 0.186, a reduction of 46.2%, while the ESG coordination index and comprehensive utility reached 0.718 and 0.759. Within this empirical and simulation setting, the results indicate that the proposed method improves ESG state recognition, risk-probability calibration, and strategic resource allocation under a limited budget.