To address the poor robustness of single feature in a complex scene and tracking failure caused by background interference and object occlusion, this study proposes a correlation filter tracking algorithm that combines adaptive feature fusion and adaptive model update. Based on kernel correlation filtering, the proposed algorithm performs weighted summation on the response maps of different features by adopting the average peak-correlation energy method to realize adaptive feature fusion of response maps. The adaptive weight is calculated as the confidence according to the peak characteristics of the response maps to determine the update rate of the model,thereby realizing the design of an adaptive model updating method. Experimental results demonstrate that the algorithm can adapt to complex scene changes, such as background disturbance, object occlusion, and rotational motion. Compared to popular correlation filtering tracking algorithms, the proposed algorithm increases the average distance and overlapping precision by 2.64% and 1.54%, respectively.