Traditional pattern recognition techniques are aimed at minimizing classification error of training samples. When the training sample is small, only the reduction of the classification error of the training sample is considered, and the transition fitting tends to occur. Support vector machine is based on the principle of minimizing the risk of experience and minimizing the range of reliability and minimizing the structured minimum risk.