Subway fire risk assessment has long depended on experts manually choosing Bayesian network nodes, which is subjective and hard to adapt. This pipeline extracts 215 fire keywords and six risk labels from accident records and fire codes with natural language processing, builds a 666-node knowledge graph, classifies nodes with a three-layer graph convolutional network, and combines confidence weighting with K-means clustering to select 22 representative nodes for the Bayesian network. Under coupled risks such as malicious passenger behavior, smoke exhaust failure and delayed alarm, the high-casualty probability rises from a 6.58% baseline to 53.2%.
Subway fire risk assessment has long depended on experts manually choosing Bayesian network nodes, which is subjective and hard to adapt. This pipeline extracts 215 fire keywords and six risk labels from accident records and fire codes with natural language processing, builds a 666-node knowledge graph, classifies nodes with a three-layer graph convolutional network, and combines confidence weighting with K-means clustering to select 22 representative nodes for the Bayesian network. Under coupled risks such as malicious passenger behavior, smoke exhaust failure and delayed alarm, the high-casualty probability rises from a 6.58% baseline to 53.2%.