To improve the tracking accuracy and robustness of correlation filter based trackers, especially with challenging factors such as occlusion, fast motion, background clutter and so forth, an improved correlation filter tracking algorithm based on deep features with local constrained mask is proposed. Based on discriminative correlation filter tracking algorithms, the learned binary matrix is proposed as a local constrained mask to achieve pruning the filter energy, which suppresses the response map generated by the template edge and the testing images. This allows the proposed method to expand the target search region 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 the mainstream methods under distractions like occlusion, background clutter and illumination changes.