Abstract: The forecast of backfill strength design can be influenced by many factors，such as high-dimensional parameters，nonlinear and random elements.In order to improve the current status of the poor prediction effect of backfill strength design，the support vector machines was adopted to establish a backfill strength design matching model with LibSVM toolbox in Matlab software.In this paper，eight major factors have been selected as condition attribute，backfill strength as decision attribute，as well as 72 sets of training samples and 6 sets of check samples are determined.With radial basis function (RBF) as its kernel function，parameters are optimized by grid search method，and the optimal parameter combination is tested through cross-validation method.The results show that：the average deviation of regression forecast and calibration made by SVM model are 1.94% and 2.23% respectively，which are of higher accuracy than the BP neural network.On the premise of ensuring the stope stability，the model based on SVM can effectively reduce cement consumption，lower backfill costs，and therefore improve economic benefits of enterprises.

Key words: Cemented tailings backfill, Backfill strength, Support vector machines, BP neural network, Prediction model