Abstract
Most coal mining enterprises in China have established and use safety production information systems for hazard identification and management, but related accident hazard data have not been fully utilized. This study is based on the classification standards defined by the "Coal Mine Major Accident Hazard Determination Standards" implemented by the Ministry of Emergency Management in 2021. We constructed a classification system including 15 major hazard categories and 79 minor hazard categories, which served as sample labels for major coal mine accident hazards. The Hybrid CNN-Transformer model was used to perform hierarchical text classification on the coal mine major accident hazard data, with the BERT model used as a baseline for comparison. The results show that in the major hazard category classification experiments, the Hybrid CNN-Transformer model outperformed the BERT model by 3 percentage points in terms of accuracy, recall, and F1 score. In the minor hazard category classification experiments, the Hybrid CNN-Transformer model achieved a maximum classification performance of 98%, generally exceeding the BERT model. The coal mine accident hazard classification algorithm based on the Hybrid CNN-Transformer model demonstrates significant classification effectiveness, providing efficient and rapid input support for coal mine major accident hazard identification systems.