Focusing on the issue that spatially regularized discriminative correlation filter (SRDCF) tracking algorithm has poor performance in handling rotation, out of view and heavy occlusion, we propose a visual tracking approach based on adaptive convolutional features. First, based on the principal component analysis of conv3-4 layer features in the VGG-NET model, the dimension of conv3-4 layer features is reduced from 256 to 130 by adaptive dimension reduction technique. Then, we maximize classifier score in the detection area and get the location and scale of target. In order to redetect the target in the case of tracking failure and achieve long-term tracking, we compare the confidence of the location with maximum score and train an online support vector machine (SVM) classifier. Finally, the tracking model is updated by the reliable tracking results which are determined by peak-to-sidelobe ratio. To verify the feasibility of the proposed algorithm, the results are compared with those obtained by thirty-eight kinds of tracking algorithms in one hundred video sequences of OTB-2015 benchmark. Experimental results indicate that the precision and success rate are respectively 0.804 and 0.607. The proposed approach has a ranking of one. Compared with SRDCF tracking algorithm, the proposed approach improves the precision and the success rate by 1.9% and 1.5%, respectively. In addition, the proposed approach is robust for rotation, out of view, heavy occlusion and other complex scenes.