Abstract
Accurate identification of coal mine safety risks is a crucial foundation for mitigating coal mine disasters. This study integrates social network analysis (SNA), the bow-tie model, and association rule mining to systematically analyze safety accident data from a coal mine. A total of 85 causative factors were extracted from 72 accidents and assessed through frequency, marginal influence, and centrality indicators to identify key risk contributors. The bow-tie model was employed to structure these causes into a safety risk control framework based on preventive and mitigation measures. Furthermore, the Apriori algorithm was applied to uncover hidden associations among gas safety risk factors, revealing critical compound relationships among factors such as inadequate safety management, insufficient inspections, high incidence of "three violations", and poor safety education. The findings indicate that management and human-related factors, particularly the absence of effective safety management systems, safety violations, and inadequate training, are the primary contributors to accidents in coal mines. Consequently, it is imperative to address these issues collectively to ensure effective risk prevention in such environments. The coal mine safety risk causality control model established in conjunction with the butterfly diagram model holds significant theoretical and practical value for coal mine safety production.