In order to design a support vector machine (SVM) classification model with better performance, the parameters and sample feature subsets that affect its classification performance are optimized, and the support vector machine theory and gravitational search algorithm (GSA) were studied. The optimal combination solution can be obtained by simultaneously optimizing the relevant parameters and effective sample feature subsets which affect the classification performance of SVM. Its effectiveness is compared and verified by experiments. The experimental results show that the proposed BGSA-SVM classification model can effectively improve the classification performance of support vector machines, which can be further extended to engineering applications.