In order to improve the tracking accuracy and scale adaptability of SiamFC++, this paper proposes a real-time target tracking algorithm with feature fusion and dual-branch template dynamic update mechanism. For tracking accuracy, feature fusion branches are designed in the shallow layer of the backbone network to improve feature extraction ability. In addition, the APCE method is used to determine whether the classification template is updated to improve the classification ability and improve the tracking effect during occlusion and deformation. For the scale adaptability, the IOU gradient ratio method and the response graph variance rate are used to determine whether the regression template is updated, which enhances the adaptability of rapid movement and scale changes. In order to ensure real-time performance, the update processes of the two branches are nested to form a dual-branch template dynamic update mechanism. The results on the data sets OTB2015 and VOT2018 show that the algorithm has a more stable tracking effect than other algorithms, and can better deal with scenes such as fast movement and occlusion. At the same time, the algorithm reaches 62 frames per second, which meets real-time requirements.