In order to improve the tracking accuracy and robustness of correlation filter based trackers, especially with challenging factors like occlusion, fast motion, background clutter and so forth, an improved Deep Features with Local Constrained Mask Correlation Filter Tracking Algorithm is proposed. Based on the classical correlation filter tracking algorithm, the learned binary matrix is proposed as a local constrained mask to achieve pruning the filter energy, which concentrates the template information and effectively alleviates the boundary effects caused by circular shifted training samples. Deep features are introduced in the process of feature extraction. By exploiting rotation, flipping, and Gaussian blur operations, the training sample set is expanded, which makes feature templates learn more target information. Compared the robustness of our algorithm with mainstream methods under distractions like occlusion, background clutter and illumination changes.