In order to improve the speed and accuracy of the hierarchical convolutional features for visual tracking algorithm, and weaken the influence of the inefficient features in different channels, an adaptive hierarchical convolutional features for visual tracking based on correlation filter framework is proposed. In this paper, we select features from two hierarchical convolutional layers representing objects, and combine the filter training with prediction. For each frame of the video sequence, the correlation filter is trained by features which are screened through the average convolutional feature ratio between the target′s region and non-target′s region in the former frame. Then the object′s position is predicted with the maximum response obtained by the classifier and the target′s features. Finally, we sparsely update the features of the initial frame in accordance with the predicted result. The proposed method is tested on OTB-100 benchmark dataset. The results show that the average distance precision is 91%, along with the average overlap accuracy 64.4% and the average speed 21.7 frames per second, which are 7.3 percentage points, 8.2 percent points higher and 11.3 frames per second faster than the original tracking method, respectively. Besides, the average distance accuracy is 1.2 percent points higher than the continuous convolution operators for visual tracking (CCOT), and the tracking speed is almost 20 times faster than CCOT. This method can improve the speed and accuracy of the convolutional tracking method effectively. It can track stably when subjected occlusion, fast moving and other interferes.