Aiming at the problem of boundary effects caused by fast motion and the model error learning under occlusion in the correlation filtering tracking framework, a multifeature joint spatiotemporal regularization correlation filtering target tracking algorithm was proposed. Using the algorithm, the features of fast histogram of oriented gradient, color names and deep convolution of the target area were extracted in the first frame, then the principal component analysis was used to reduce the dimensions of the features; at the same time, the spatiotemporal regularization was added to the correlation filtering tracking framework in order to alleviate the problems of boundary effects and model degradation in the tracking process. Finally adaptive scale estimation was performed with the help of scale pool method. The experimental results prove that the proposed algorithm performs effectively under the circumstances of scale variation, occlusion and fast motion.