OBJECTIVE: The objective of this study is to conduct precise risk prediction of traffic accidents using vehicle trajectory data. METHODS: For urban road and highway scenarios, a scheme was developed to gather vehicle kinematic data and driving operation records from an in-vehicle device. The raw trajectory samples of over 3000 vehicles were processed through cleaning, filtering, interpolation, and normalization for preprocessing. Three deep learning frameworks based on RNN, CNN, and LSTM were compared. An end-to-end LSTM accident risk prediction model was constructed, and the model was trained using the cross-entropy loss function with Adam optimizer. RESULTS: The LSTM model is capable of directly extracting accident-related hazardous state features from low-quality raw trajectory data, thereby enabling the prediction of accident probability with fine-grained time resolution. In tests conducted under complex traffic scenarios, the model successfully identifies high-risk driving behaviors in high-speed road sections and intersections with a prediction accuracy of 0.89, demonstrating strong generalization performance. CONCLUSIONS: The LSTM accident risk prediction model, based on vehicle trajectory, developed in this study, is capable of intelligently extracting dangerous driving features. It can accurately warn about the risk of traffic accidents and provides a novel approach to enhancing road safety.