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Roof accidents, as typical accidents in mine safety production, occur frequently and are highly harmful, posing a significant threat to the life safety of miners. Through the causal analysis of the occurrence process of roof accidents, this study creatively constructs an accident causation matrix to realize the characteristic description of accident causes, which serves as the data support for the Bayesian network built based on fault tree modeling. Ultimately, a new analysis framework integrating the accident causation matrix and the Bayesian network model is established. In the process of accident analysis, first, based on the 2-4 causation model theory and combined with the association rule algorithm, the key factors of the accident and their internal correlations are obtained, and the accident causation matrix is constructed. Second, the fault tree is transformed into a Bayesian network model, and the accident causation matrix is used for parameter learning and optimization. Finally, two methods-model comparative analysis and real case verification are adopted to prove the advancement and effectiveness of this study. Researching results indicate that the accident causation matrix can effectively characterize accident causation factors, providing precise input data for Bayesian network models and significantly enhancing their reliability. Through the reverse reasoning function of Bayesian networks, dynamic diagnosis of accident causes and identification of key risk factors are achieved, enabling a more dynamic and detailed analysis of accident causes. This offers a scientific basis for coal mining enterprises to formulate preventive measures.