Siamese trackers usually use the target in the first frame as a fixed template, but the static template cannot adapt to target changes. The existing updater is challenging to deal with target deformation and update noise, and there is an excellent risk of updating with an inaccurate updater. In our research, a dynamic template updating strategy based on spatial-temporal information is proposed to improve the tracking accuracy of the Siamese tracker. Furthermore, Tracking Confidence Network (TCNet) is proposed to judge whether to update, which ensures that high-quality target features are used to update and reduce the noise caused by adding unreliable targets. In experiments, the proposed method is embedded into two baseline trackers: SiamRPN and SiamFC++, and tested on five popular benchmarks. The experimental results show that the proposed method can improve the performance of the Siamese trackers while maintaining real-time speed.