Abstract: In order to predict the outburst risk dynamically by using gas real-time monitoring data after blasting in blasting driving face,this paper proposed a method to automatically identify blasting event in the gas concentration monitoring curve by applying the first-order difference,convolution operation and the findpeaks function in MATLAB. By using the auto-identification method,the blasting event in a blasting driving face is automatically identified and extracted. Then the recognition result is evaluated. Based on the results of blasting identification and extraction,analyzed the variation characteristics of the gas concentration after blasting,and calculated the correlations between the growth rate,peak value,decay rate of gas concentration after blasting and K1 value,and based on these three indexes,a prediction model of outburst risk for blasting driving face is established and applied to another face to predict the outburst risk in front of the working face. The results show that the proposed auto-identification method realized the automatic identification and extraction of the blasting moment,the gas concentration at the blasting,the peak value and the moment of the gas concentration after the blasting. The recall and precision of the proposed auto-identification method are 84.13% and 77.21% respectively,and the mean absolute errors of the extracted peak gas concentration after blasting and the blasting time are 0.018 9% and 2.323 7 min,respectively. The growth rate,peak value,decay rate of gas concentration after blasting are all strongly related to the K1 value,and the correlation coefficients between three indexes and K1 value are 0.85,0.92 and 0.79 respectively. Applying the established outburst risk prediction model,25 times jet hole and 1 time small outburst were successfully predicted,with prediction efficiency of 60.48%. The results can be used as a supplement to the existing outburst risk prediction methods,assist K1 value in prediction of outburst risk,and provide guidance for the identification of abnormal gas and the prediction of outburst risk in blasting driving face.