We propose a multiscale fast correlation filter tracking algorithm based on fusion features to solve the problem of poor tracking effect caused by target occlusion and scale change in complex scenes. Firstly, the dimensions of the three features of the target are reduced and fused to form a feature matrix. Secondly, the principal component analysis is used to extract the salient features in real time, reconstruct the feature matrix, and position correlation filters are trained while reducing the dimension effectively. Finally, the fusion feature matrix is adopted to train scale correlation filters, thus the position and scale of the target is accurately predicted. We compare the improved algorithm with popular correlation filter tracking algorithms by experiment, and the results show that the improved algorithm has a higher tracking accuarcy and an average tracking speed of 52.5 frame/s in scenarios of target occlusion and scale change.