To solve the limitation of the feature fusion target tracking (Staple) algorithm when using the fixed weight fusion method in complex scenes, this study proposes an adaptive-feature fusion and channel weighted anti-occlusion related filtering algorithms with an improved channel confidence. First, to introduce a multi-dimensional feature description, we calculate the channel weights according to the response peak of the filter template on each channel. Then, calculate the reliability of the model based on the response results of the feature model, determine the fusion weight of the model, and complete the feature fusion from the perspective of the response results. Finally, based on the average peak correlation energy of the historical frame and the mean square error of the current and previous frame images, we determine the occlusion of the target and update the model. Comparative experiments with the Staple algorithm are conducted on the OTB-2013 and OTB-100 datasets and the proposed algorithm suggests an improved success rate and accuracy and performs better with respect to many challenging attributes.