Aiming at the problem that the classification accuracy in malware behavior analysis system was low,a malware classification method based on Support Vector Machine (SVM) was proposed. First, the risk behavior library which used software behavior results as characteristics was established manually. Then all of the software behaviors were captured and matched with the risk behavior library, and the matching results were converted to data suitable for SVM training through the conversion algorithm. In the selection of the SVM model, kernel function and parameters (C,g), a method combining the grid search and Genetic Algorithm (GA) was used to search optimization after theoretical analysis. A malware behavior assessment system based on SVM classification model was designed to verify the effectiveness of the proposed malware classification method. The experiments show that the false positive rate and false negative rate of the system were 5.52% and 3.04% respectively. It means that the proposed method outperforms K-Nearest Neighbor (KNN) and Naive Bayes (NB); its performance is at the same level with the BP neural network, however, it has a higer efficiency in training and classification.