Target tracking is a research hotspot in the field of machine vision. How to improve the tracking level in complex scenarios is a challenging problem. Previous studies have shown that how to use features effectively is the key to tracking. Therefore, a target tracking algorithm based on channel fusion features is proposed. Based on the multi-channel correlation filtering framework, the method introduces the feature channel weight, adjusts the weight according to the contribution of the channel to the response value, and then constructs the real-time feature combination. The algorithm can capture the state change of the target quickly and track the target effectively. In order to verify the effectiveness of the algorithm tracking, we test the algorithm performance on the open dataset OTB2015 and compare it with a variety of tracking algorithms. Experimental results show that the algorithm has sound tracking accuracy and success rate, and the overall performance is better than the compared algorithm.