This study proposes an analysis method based on association mining between historical accident reports and a root cause index system to fully leverage experts’ experience in root cause analysis of past accidents and enhance the accuracy and comprehensiveness of such analysis, thereby reducing chemical safety incidents. By constructing an association matrix between accident reports and the index system, this method utilizes a pre-trained model to represent accident and index texts. It integrates secondary and tertiary index information based on an attention mechanism and finally employs a graph convolutional neural network for root cause analysis. Validation on a dataset of 1351 samples demonstrates that this method significantly improves the accuracy of root cause prediction, effectively utilizing expert analysis of historical accidents to analyze current accidents and uncover the limitations in previous accident analysis. Additionally, this method accurately identifies the root causes of accidents even with incomplete incident descriptions. The application of this method will enhance accident prevention and risk management in occupational safety.