When the target tracking algorithm deals with occlusion, blur, scale transformation, and other challenges, it can easily result in drift and tracking failure; thus, an adaptive correlation filtering tracking algorithm for complex scenes is proposed. First, the proposed multi-feature complementarity method is employed to train the corresponding filters with features, and the fusion weight of features is dynamically adjusted according to the response value of each filter to complete the position estimation for the target. Then, a scale filter is constructed with the center position to estimate the optimal scale of the target. Finally, the multi-scale search region method is integrated, and the tracking model is selectively updated according to the tracking confidence degree, which further enhances the performance and anti-occlusion ability of the tracker. Tests were performed on 74 color datasets of OTB2015 and the proposed algorithm was compared with the advanced correlation filtering algorithms recently. The average distance accuracy of the proposed algorithm is 0.801, the average overlap accuracy is 0.715, and the real-time tracking speed is 39.24 frame/s. Experimental results show that the tracker performs well in a complex environment and has an excellent overall performance.