Aiming at the problem that the tracking algorithm based on the fully convolutional siamese network is easy to tracking drift in the face of complex environments such as analog interference and illumination changes, this paper proposes the following strategies to optimize features on the basis of analysis and experiments. First, the deep convolutional neural network VGG16 is introduced into the tracking framework to improve the feature extraction ability of the model. Then, aiming at the problem that a single feature cannot adequately describe the target information and is sensitive to interferences, this paper designs a feature enhancement module, which integrates different levels of semantic information from shallow to deep to improve the expressiveness of features. Finally, a lightweight triple attention is proposed to help the model adaptively focus on dominant features and further improve the robustness of the model in complex environments. Applying the above strategies to the fully convolutional siamese network algorithm has achieved remarkable results. On the OTB100 dataset, compared with benchmark algorithm, the area under the success rate curve of the algorithm in this paper is increased by 15.1%, and the distance accuracy is increased by 16.3%, and the target can also be effectively tracked in complex environment.