Aiming at the problem of decreased corner positioning accuracy caused by light changes, fast motion and scale changes in the tracking process, we propose a visual tracking algorithm motivated by the framework of SiamCAR. Firstly, the research method uses improved ResNet-50 as a feature extraction backbone network and combines with enhanced multi-layer fusion feature map to extract feature, which makes full use of the location information of shallow features and deep semantic information of the network, and improves the semantic understanding ability of the algorithm to target features. Secondly, a hybrid attention block is constructed to alleviate the inaccurate corner location of the anchor tracker, which improves the tracking accuracy and positioning accuracy of the algorithm. Finally, extensive experiments are carried out on GOT10K, UAV123, LaSOT and other datasets. Besides, compared with current advanced trackers, the proposed algorithm can better resist the influence of various complex factors such as illumination variation, rapid motion and scale variation, at the same time, obtain good tracking performance on a number of evaluation indicators.