To address the complex and uncertain causes of safety accidents in chemical enterprises, this study applied text mining techniques to systematically extract 29 causative factors from 422 accident reports. These factors were classified into five categories: personnel issues, resource management deficiencies, adverse organizational atmosphere, organizational process flaws, and inadequate supervision. Based on the extracted factors, a complex network model of accident causation was constructed. Using degree centrality, betweenness centrality, and eigenvector centrality, seven core causative factors were identified, along with multiple peripheral factors closely linked to them. Bayesian network-based sensitivity analysis further revealed the factors that exert the greatest influence on accident occurrence, and subsequent path analysis uncovered several critical accident propagation paths. The findings reveal core causative factors and critical propagation paths, which may inform the prioritization of risk control measures under conditions of limited resources.
Ni et al. (Mon,) studied this question.