The backfill of a drill-hole is a throat engineering process in which the filling slurry is transported to the underground stope from the surface to ensure the safety of the mine normal operation. To predict the service life of the mine backfill drill-hole, a combination forecasting model of the Support Vector Machine (SVM) and the BP neural network is established in this paper. The mean square error of the value is taken as a fitness function of the SVM. Then, the SVM model parameters are optimized through the Genetic Algorithm(GA). Then, the optimized SVM is applied to predict the prediction set. The final forecast result is obtained by means of the revision of the residual error through the BP neural network. A certain mine is taken as an example, its drill-hole life is predicted through the combination forecasting model, and the optimal parameters are obtained. The adaptive value (mean square error mse) is 0.0111; the penalty coefficient C is 47.0768; the kernel function parameter σ is 2.2638. The accuracy of the model is analyzed. The relative error of the predicted results is about 3%. Compared with the single prediction model, the combination forecasting model enjoys a higher accuracy.