Aiming at the imbalance of training data set, an integrated support vector machine classification algorithm was proposed by combining sampling method with ensemble method. Firstly, unsupervised clustering was performed on an unbalanced training set, then the underlying local attention support vector machine was used to partition the data set so as to precisely control the local features of data sets. Finally, top support vector machine was used to predicte classification. The evaluation results on UCI dataset show that compared with the popular algorithms such as sampling based Kernelized Synthetic Minority Over-sampling TEchnique (K-SMOTE), integration based Gradient Tree Boosting (GTB) and cost sensitive ensemble algorithm (AdaCost), the proposed support vector machine algorithm can significantly improve the classification effect and solve the problem of unbalanced data set to a certain extent.