The traditional long-term correlation filter uses a single feature and cannot capture the target again after tracking failure. Considering this, the paper proposes a multi-feature fusion long-term target tracking algorithm combined with deep learning. On the basis of the long-term correlation tracking (LTC) algorithm, the proposed algorithm uses multi-feature fusion to join together the local binary pattern feature, the improved directional gradient histogram feature, and the color feature to promote the robustness of the tracking algorithm. Since the LCT algorithm adopts a random fern classifier to recheck the target, which has a limited detection range and low rechecking accuracy, the deep learning-based twin network instance search (SINT) method is employed to recheck the global image. The experiment in this paper is carried out on the OTC100 dataset, and the results show that compared with the LCT algorithm, the proposed algorithm has improved the range accuracy and the success rate by 13% and 10.3% respectively.