Abstract: Coal mine gas accidents are highly destructive and have a wide range of hazards. Predicting the amount of gas emitted from the mining face and the volume fraction of gas in the return air roadway can provide important basis for formulating gas control measures and preventing gas accidents. To solve the problem of mining face gas emission and gas volume fraction in mining face return air being affected by multiple factors and difficult to accurately predict due to large data fluctuations, we introduce the sparrow search algorithm (SSA) based on grey prediction and BP neural network, and establish an ISSA-GM-BP model for predicting the mining face gas emission and gas volume fraction in mining face return air roadway. This model utilizes Chebyshev chaotic mapping, dynamic inertia weight, and Lévy flight strategy algorithm to improve SSA. In grey prediction, a dynamic grey GM (1,1, \alpha ) model is established by introducing dynamic generation coefficients and combined with BP neural network. The combined model is then optimized by improving SSA. Use this model to predict the mining face gas emission and gas volume fraction in mining face return air roadway, and compare and analyze the prediction results with SSA-BP neural network and BP neural network. The results showed that in terms of mining face gas emission and gas volume fraction in mining face return air roadway, the average relative errors between the prediction results of the ISSA-GM-BP model and the measured values were 2.95% and 2.65%, respectively. The average relative errors of the SSA-BP neural network were 9.50% and 8.00%, respectively. The average relative errors of the BP neural network were 12.49% and 9.76%, respectively. The determination coefficients of the ISSA-GM-BP model were 0.960 9 and 0.958 7, respectively. The predicted values fully conform to the trend of actual mining face gas emission and gas volume fraction in mining face return air roadway, and have significant advantages in prediction accuracy and adaptability.