Risk assessment of civil aviation cabin safety incidents based on the CNN–LSTM–Attention model - Lianbin Zhou; Xi Wang; Weiwei Jiang; Peiwen Zhang (PLoS ONE, 2026)
Against the backdrop of the thriving global civil aviation industry, cabin safety, which is a crucial link in civil aviation safety systems, has prompted an increasingly urgent need for intelligent risk management. This study constructs an integrated framework of “deep text mining–intelligent risk assessment” and proposes a hybrid CNN-LSTM-Attention model for cabin safety incident classification. Using 8,280 global cabin abnormal event reports from 2004–2024, the model achieves 95.01% accuracy and an F1 score of 94.17% on the test set, substantially outperforming benchmark approaches. Compared with XGBoost (89.45% accuracy, 88.98% F1), the proposed model improves accuracy by 5.56% and the F1 score by 5.19%, demonstrating superior capability in extracting deep semantic features and identifying key risk patterns. The framework also establishes a “text feature–risk mechanism-hazard level” mapping system, enabling more interpretable risk quantification. The findings provide an effective technical path for intelligent early warning of cabin safety risks and offer methodological support for data-driven decision-making in civil aviation safety management.