In order to improve the accuracy and real-time performance of visual tracking in complex scenes, a real-time and anti-occlusion visual tracking algorithm based on multi-layer deep convolutional features is proposed. For the visual tracking task, the deep convolutional networks VGG-Net-19 are fine-tuned, and then the multi-layer deep convolutional features of the target region are extracted from the adjusted model. The location correlation filters are constructed to determine the target center position. In order to determine the target scale, a scale correlation filter is performed to sample multi-scale images surrounding the target region. When the target is occluded, the stage evaluation strategy is used to update and recover the model, which solves the problem of template error accumulation. The experimental results on the tracking benchmark OTB-2015 which concludes 100 video sequences and UAV123 which concludes 123 video sequences show that the proposed algorithm has higher accuracy and can adapt to complex situations such as target occlusion, appearance change and background clutters. The average speed is 29.6 frame/s, which meets the real-time requirements of the visual tracking task.