Aiming at the failure of existing hierarchical convolutional features for the visual tracking algorithm in complex environments, an adaptive object tracking algorithm based on the spatial attention mechanism is proposed. According to the color histogram of the current frame, the spatial attention mechanism is established based on the Bayesian classifier. After extracting multi-layer convolutional features in VGGNet19, the spatial attention map is fused with convolutional features respectively to construct more robust target apparent models. The response is obtained by using the correlation filter, and the final response is achieved by the weighted summation criterion. The adaptive update of the filter template is implemented by using the frame difference method to adjust the learning rate in the tracking process. The experimental results show that the tracking accuracy and robustness of the proposed algorithm are better than the existing state-of-the-art tracking algorithms in most complex environments.