To identify the key causative factors and their characteristics across different types of chemical accidents, text mining techniques were first applied to extract causative factors from accident investigation reports. The extracted factors were then classified according to an improved Human–Machine–Environment–Management (HMEM) framework, which incorporates an additional government influence layer. To address data imbalance, a random undersampling method was employed. Specifically, sampling was repeated 30 times using different random seeds, and association rule mining was conducted for each sampled dataset. On this basis, a hybrid analytical framework integrating the Apriori algorithm and complex network theory was developed to examine the topological characteristics of the causation network. The results indicate that the network exhibits both small-world and scale-free properties, with strong interconnections among causative factors and a limited number of key nodes playing important bridging roles. PageRank centrality analysis further reveals that nodes associated with all accident types are located in the core region of the network, although differences exist in the associated causative factors across different accident types. In addition, the comprehensive importance analysis indicates that D6 (illegal production organization), B5 (pipeline rupture or blockage), and D12 (unsafe work practices) are the top three most important causative factors. These findings provide a theoretical foundation and practical insights for chemical accident prevention and the improvement of safety management.
Liu et al. (2026) studied this question.