Construction and analysis of a coal mine accident causation network based on text mining

It is important to systematically identify the contributing factors in coal mine accidents from a large-scale analysis of accident reports. However, previous scholars have mainly used human analysis methods to define accident-causing factors, leading to incomplete and biased cause checklists due to personal experience and knowledge. Furthermore, a data-driven method is needed to quantify the importance of each factor and clarify the mechanism of different types of accidents. Considering these, this study creatively combined text mining technology and a complex network to explore the coal mine accident-causing mechanism. Through the text mining of 307 accident reports, 52 main accident-causing factors were identified, and a coal mine accident causation network was constructed based on the strong association rules among factors. Second, eight core factors and their associated sets, as well as seven critical links for different accident types, were clarified through network centrality analysis and accident path analysis. This study shows that regulatory authority is the most influential level of accident causation, gas accidents are the most easily triggered accident type, a lack of effective mechanism for safety supervision→failure to arrange full-time safety inspectors to follow the shift→lack of serious and thorough on-site hidden danger investigations→inadequate anti-surge measures are the key links in gas accident causation. This study contributes new perspectives on identifying contributing factors and their complex interaction mechanisms from accident report data for practical applications in risk analysis and accident prevention.