Herein, we propose a target-tacking algorithm based on adaptive updating of multilayer convolutional features to address the insufficiency of traditional manual feature expression and the error accumulation of filter models. First, the algorithm uses a layered convolutional neural network to extract the image features, and fuses multi-convolution features through linear weighting to predict the target position. Then, the multiscale target convolution features are used to determine the target optimal scale. Finally, the average peak correlation energy is used to evaluate the confidence of the target response. We evaluate the motion condition of the target according to the frame differential mean and displacement of the two adjacent frames of the target image, and adjust the learning rate of the filter model according to the predicted position credibility and the appearance of the target image. The performance of the algorithm is verified using the OTB-2013 public test set and compared with the existing mainstream moving target tracking algorithm based on correlation filtering. Experimental results show that the proposed algorithm provides higher accuracy and success rate, and is more robust in complex cases.