Abstract: The prediction and pre warning of the coal burst are helpful to mastering the degree of disaster risks and taking some prevention measures in advance, which is important for reducing the disaster consequences. The real time quantitative predictions for the coal burst hazard level, hazardous zone and occurring time are key issues for the coal burst prediction. This study proposes a “dual driven” intelligent pre warning framework which integrates the physics driven and data driven models, predicting the disaster hazard level, hazardous zone and occurring time. Under the framework of physics driven, this study introduces the Bayes probability model performing a dynamic static, synergistic, real time evaluation for the coal burst hazard level using field parameters including micro seismic data, real time stress data and seismic CT micro seismic detection data. Under the framework of the data driven, based on the quantitative predictions of micro seismic events, this study builds a deep learning model MSNet which combines a vanilla convolutional module, recurrent neural network modules and autoregressive module. The MSNet takes historical micro seismic data as model input and quantitatively predicts the time, zone and energy for imminent micro seismic events whereby determining the potential time and location for the coal burst. This study also develops an intelligent pre warning platform which embeds the “dual driven” model for the coal burst for a deep coal mine using Unity3D software. This study proposes a novel intelligent coal burst technology based on the integration of physics driven and data driven. The developed pre warning platform can predict and display the coal burst hazard level, hazardous zone and potential time in a real time. The application of the platforms demonstrates the success in a long wall workface with the predicting accuracy of hazard level reaching 0.88. The predicting accuracies for micro seismic event coordinates and time are 0.977 and 0.523, respectively. The predictions for the potential coal burst zone and time based on the micro seismic event predications are consistent with the field log, which indicates the feasibility of “dual driven” model in the project field.