Abstract: In order to solve the design problem of mine backfill strength and improve the adjustment ability of mine backfill strength, the SVM intelligent prediction model of backfill strength is established by investigating the actual data of backfill strength of 100 mines in China.The BP neural network model is compared with the predicted results of the SVM model by training the data of 70 samples.The results show that the maximum error of the SVM prediction model is 3.52% and the average error is 2.41%, the BP prediction model is 10.98%, and the average error is 7.01%.The SVM model has higher prediction accuracy and less error than the BP model.The SVM model is used to predict the backfill strength of Sanshandao gold mine, the strength of the backfill in the mining room is 1.02 MPa in one step, the recommended sand-cement ratio is 1∶12, and the strength of the backfill in the second-step mining room is 0.86 MPa, the recommended sand-cement ratio is 1∶16.The filling effect of the on-site stope is good and no backfill body instability occurred.The intelligent matching model of the backfill strength based on SVM can reduce the filling cost and improve the economic benefit of the mine under the premise of meeting the stability of the stope.