Siamese network is widely used in object tracking. However, it suffers the problem of poor generalization. The existing tracker based on template update mechanism uses complex calculation strategies and time-consuming optimization to achieve good tracking performance, but it does not meet the requirements of real-time tracking. In this paper, we first propose a tracking framework based on space-time history frames, and use feature enhancement to enrich the features of history frames. Then, we propose EnhanceNet which is an offline trained network for performing online data augmentation. It can enhance the tracking accuracy while preserving high speeds of the state-of-the-art online learning. In challenging large-scale datasets, such as LaSOT, VOT2018 and OTB-2015, our framework is better than state-of-the-art real-time trackers and has achieved excellent results while running at 32 FPS.