Support Vector Machines is an effective supervised learning classifier by random selective labeled samples, with increasing information capacity and development of data collection ability, however this passive learning method which learns from samples needs cost much labor to label large-scale samples in actual application. To the problem that large-scale labeled samples isn’t easy to acquire in the course of SVM training, the active learning strategy is used in the SVM training and an incremental training algorithm of active SVM based on the uncertainty based sampling of distance ratio is proposed in the paper. The experimental results show that the active SVM learning strategy can considerably reduce the labeled samples and costs compared to the passive learning method, and at the same time it can ensure the accurate classification performance is kept as the passive SVM and also expedite the SVM training.