Abstract: In order to improve the prediction accuracy of coal mining face gas outflow, a coal mining face gas outflow prediction method based on quadratic decomposition and BO-BiLSTM combination model is proposed. Firstly, variational mode decomposition （VMD） is used to decompose the gas outflow time series data once, making full use of the residual component after decomposition, and using adaptive noise complete empirical mode decomposition（CEEMDAN） for secondary decomposition. Then, all the subsequences after decomposition are input into the Bayesian algorithm optimized bidirectional long short-term memory network （BO-BiLSTM） model for gas outflow prediction. Finally, the output results of each subseries model are superimposed to obtain the final gas outflow prediction results. Taking the daily monitoring data of absolute gas outflow in a mine face in Binchang Mining Area of Shaanxi Province as an example, the results show that the proposed combined prediction model of gas outflow has high prediction accuracy, which verifies the effectiveness and applicability of the model in the prediction of gas outflow.