This study aims at resolving the tracking failure caused by the coexistence of similar targets and the significant change in the appearance of a target based on the full convolution siamese (SiamFC) network algorithm. An online adaptive siamese network tracking algorithm (AAM-Siam) based on attention mechanism is proposed to enhance the discriminative ability of the network model and achieve the online learning target appearance change and suppress background. Firstly, the results obtained by tracking the previous frame are added into the template branch and the search branch respectively to compensate for the shortcomings of the network by responding to the changes in the appearance of the target. Secondly, the spatial attention module and the channel attention module are employed into the siamese network to achieve the feature fusion among various frames, learn the target deformation online and suppress background, as well as enhancing the model''s ability to express features. Finally, detailed experiments are conducted on the online tracking benchmark (OTB) and visual object tracking 2016 (VOT2016) benchmark. The experimental results indicate that the accuracy and average success rate of the proposed algorithm on the OTB50 dataset are 4.3 and 3.6 percentage points higher than those obtained using the basic SiamFC network algorithm, respectively.