Deep learning has revolutionized the performance of classification, but meanwhile demands sufficient labeled data for training. Given insufficient data, neural network is apt to overfitting, which is quite general in low data regime. We propose a data augmentation technique based on generative adversarial network to address the network training and data shortage problem. The experimental results show that the synthesized data has semantic similarity compared with the real data, and at the same time it can present the diversity of the context. After adding the synthesized data, the neural network can be trained more stably, and the accuracy of the classification is further improved. Comparing the proposed algorithm with some other data augmentation techniques, the proposed method has the best performance, which proves the feasibility and effectiveness of this technique.