A weight response target tracking algorithm based on adaptive feature fusion is proposed to address the problem of drift or tracking loss due to the difficulty of extracting rich feature information by the tracker in complex scenes.Firstly,an improved VGG16 network is used to improve the discriminative ability.Secondly,a residual semantic embedding module is employed to introduce deep semantic information into shallow features,and then the shallow feature response and deep feature response are weighted and fused to improve the localization accuracy and discriminative ability.The experimental results show that,compared with the benchmark algorithm,the evaluation indexes of the proposed algorithm,such as tracking success rate and accuracy,are both improved on the OTB2015 and VOT2017 datasets.