In order to effectively solve the problem of target tracking drift or loss in the face of large-scale deformation, complete occlusion, background interference, and other complex scenes, a multi-branch Siamese network target tracking algorithm (SiamMB) is proposed. First, the method of enhancing the network robustness of adjacent frame branches is used to improve the discrimination ability of target features in the search frame and strengthen the robustness of the model. Secondly, the spatial attention network is fused, and different weights are applied to the features of different spatial positions. In addition, the features that are beneficial to target tracking in spatial positions are emphasized, so as to improve the discriminability of the model. Finally, evaluation is performed on OTB2015 and VOT2018 datasets, and the results show that the tracking accuracy and success rate of SiamMB reach 91.8% and 71.8%, respectively, which makes SiamMB more competitive than the current mainstream tracking algorithms.