Typically, networks are sensitive to targets being blocked or interference around the target when tracking moving targets, resulting in unreliable response positions and incorrect tracking frame. Thus, an anchor-free Siamese network-tracking approach based on deep learning is proposed. First, the feature weight of the target guidance is derived through the nonlocal perceptual network, which is then applied to refine the depth features of the target template branch and search branch, and to improve the remote dependence of the two branch features in a supervised manner to effectively suppress noise interference. Second, to correlate the multidimensional regression features with the tracking quality, a bounding box perception block is developed. This module strengthens the interaction between the target template branch and the search branch and enhances the accuracy of network positioning. Furthermore, the proposed method can track the target in real time and enhance accuracy, according to the experimental findings on standard data sets.