Tracking algorithms with Siamese network use the offline training network to extract features from the target for matching and tracking. In the offline training process, the network learns the common features of similar goals. In the case of interference from similar targets, using common features to express specific targets will lead to degradation of tracking performance and even loss of targets. To improve the feature discriminative ability for similar targets, we update the parameters of network online, and make the network further learn the specific characteristics of the current target based on the common features. The proposed method can not only effectively distinguish the target and background, but also eliminate interference from similar targets. We conduct a large number of experiments on the OTB50 and OTB100 databases. The results show that the proposed algorithm can improve the discriminative ability to features extracted by the network and achieve robust tracking of the target.