Abstract: Support Vector Machine is small sample method based on statistic learning theory. It is a new method to deal with the highly nonlinear classification and regression problems. It can better deal with the small sample, nonlinear and high dimension, avoiding the problem of local minima that the neural network can not solve. This paper briefly introduced the Support Vector Machine and the basic tenets of the infiltration flow monitoring data processing applications, described how to make use of Support Vector Machine to build dam seepage statistical model and forecasting. By calculating and analyzing on the continuous seepage monitoring data of Yunlong reservoir dam and comparing with RBF neural network forecasting result, the result showed that this method has higher precision and generalization ability than RBF neural network.