Recently, the framework of convolution neural network has been successfully applied to the target tracking, and has achieved robust tracking results. On the basis of this conception, a target tracking method based on location-classification-matching model is proposed. First of all, in the location model,the candidate target region of the current frame is predicted by using location information of previous frame. Secondly, the trained deepth features are used to inter-class screen the candidate regions, and N sub-optimal target regions are selected. Finally, we use conventional color features to perform intra-class optimization matching for sub-optimal target regions, so as to determine the final tracking target. Meanwhile, the network in the location and the classification is updated separately, and the established target model is updated online and real-time to ensure that the model describes the target accurately. Experimental tests are performed on OTB50 and OTB100 standard databases, the experimental results show that the proposed tracking method has better tracking robustness under the conditions of fast motion, similar object interference, and complex background.