Abstract: Under the background of coal mine intelligent construction, how to efficiently extract adequate information from the massive monitoring data of coal burst and improve the accuracy of prediction and warning is the focus and difficulty of future research. Aiming to solve the challenges of the poor generalization ability of current physical model-based coal burst prediction methods and insufficient feature mining of massive data, combined with deep learning technology, a time series coal burst prediction method driven by the fusion of physical model and data feature was initially established. In this paper, taking a strong coal burst risk longwall face in Binchang Mining area, Shaanxi Province as the background, the variation characteristics of physical indicators before several large energy events are analyzed, and the shortcomings of coal burst risk prediction indicators driven only by physical models are statistically analyzed. The coal burst time domain prediction method driven by the fusion of physical model and data feature is proposed. The prediction model includes three modules: data preprocessing, feature extraction, and early warning model construction−the data preprocessing processes the original microseismic monitoring data into precursor pattern sequence within a specific time window. Feature extraction based on a physical model mainly includes the characteristics of explicit and implicit data feature extraction based on convolution neural network. The explicit and implicit characteristics of the depth of the fusion method was put forward based on attention mechanism, and through the whole connection network forecast model classification, the impact of different risk levels of large energy events. The model test results show that the prediction F₁ score can reach 0.956, 0.950, and 0.854, respectively, when the prediction time is one day, two days, and three days in the future. The site can choose the prediction time according to the demand. In engineering application, the model can accurately predict large energy events, and the error analysis results show that the prediction accuracy of the model is high, which can meet the needs of the site.