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The granular support vector machine (GSVM) can effectively improve the learning efficiency of support vector machine (SVM) but may lose some generalization ability at same time, because it is sensitive to the initial granulation parameter and the selection of granular centers is rough. This paper proposes a new granular support vector machine model, called affinity propagation based granular support vector machine (APG_SVM). Firstly，a group of high-quality and more representative granular centers are selected to join the training dataset using affinity propagation. And then the training dataset is optimized according to the mixing degree of sample in the granular and the distance between the granular centers and hyperplane. The final training dataset is generated, and the generalization performance of GSVM can be improved by training on the final dataset. The experimental results on UCI standard datasets show that compared with traditional GSVM, the classification efficiency of this algorithm is obviously improved, the accuracy on several datasets is relatively stable, and the classification performance is better.