ing to improve the tracking accuracy of the correlation filter tracking algorithm when faced with occlusion, background clutter, and deformation of the object target, this study proposes a correlation filter tracking algorithm for multiple features based on temporal consistency and spatial pruning. First, in the training stage, the energy distribution of the filter template is pruned using the binary matrix mask to make the template information more concentrated in the target area, which alleviates the boundary effect caused by the cyclic shifted samples. Second, the l₂-norm is used as the temporal consistency model to establish smoothness constraints for the filters of two consecutive frames so that filter templates can learn the context information of consecutive-frame target and increase the anti-interference ability of the algorithm. To further improve the expressive ability of the target template, ResNet50 deep features, which contain rich semantic information, are introduced into the tracking framework. Principal component analysis is used to reduce the dimension of the extracted depth feature, and traditional features in combination with deep features improve the accuracy and robustness of the tracking results. A comparison of the proposed algorithm with five existing algorithms verifies the proposed tracking algorithm’s robustness in dealing with distractors such as target occlusion, background clutter, and deformation.