Abstract: A new data mining method of Support Vector Machines(SVM)is applied on the classification of rock mass in tunnels.SVM is a novel powerful leaning method that based on Statistical Learning Theory.SVM can solve small-sample learning problems better than neural network. Parameters including rock layer thickness,rock mass structure,inlay condition,weathering condition,groundwater characteristic,joint condition,hammer knocking sound and ground stress,are chose as the judge factors.Data samples from Niba Mountain tunnel are used to train the SVM with different kernels. The mapping relationship between judge factors and rock mass classes is used. The SVM can discriminate and provide class-unknown data samples of rock mass. Result of the classification shows that SVM with polynomial kernel has a high accuracy when it is used to classify the rock mass.So this is an intelligent classification of rock mass method that can be applied to classify rock mass in tunnels.