Aiming at the problem that object tracking with single image feature under complex circumstances has low accuracy and poor robustness, a correlation filtering object tracking algorithm based on multi-feature fusion is proposed. Firstly, histogram of oriented gradient (HOG) features, color histogram features and convolutional features are respectively extracted from the target and background regions, and a fixed-coefficient fusion strategy is adopted to combine the feature response maps of HOG features and color histogram features. Then the fused response map and the convolutional features response map are fused by adaptive weighted fusion strategy，and the scale estimation algorithm is used to solve the problem of target scale changes. Finally, the sparse model update strategy is used to update the model. The proposed algorithm is evaluated on OTB-2013 dataset and compared with state-of-the-arts object tracking algorithms. Extensive experimental results show that our method significantly improves the performance in median distance precision and median overlap precision compared to the optimal algorithm. The accuracy and robustness of the proposed algorithm are superior to those of other algorithms in complex scenarios because of the effective use of HOG, color histogram and convolutional features.