Accurate prediction of multi-parameter gas extraction in mines is vital for intelligent gas extraction regulation. This study establishes a linear and nonlinear multi-parameter prediction model to address excessive prediction residuals resulting from parameter interactions. Initially, a combined linear-nonlinear model is constructed using ARIMA and RBFNN. Real-time multi-parameter data under varying negative pressures is collected via a custom experimental platform. After preprocessing and inspection, the linear models is obtained: ARIMA (0,1,1) for gas concentration, ARIMA (0,1,1) for flow rate, ARIMA (2,1,2) for air leakage volume. Finally, the prediction residuals were optimised using RBFNN to obtain the combined model predictions. Results demonstrate closer proximity of combined model predictions to actual values, with gas extraction multi-parameter R2 surpassing 0.7. Additionally, negligible values of MAE、MSE and MAPE attest to the model’s robust performance, laying theoretical groundwork for dynamic gas extraction regulation and control.