Single target tracking algorithm based on Siamese architecture suffers from untimely target state update. To address this issue, a generic template update mechanism is proposed based on the dynamic fusion of templates and memory information. The mechanism uses a dual module fusion update strategy. The short-term memory information of search feature map is fused using a memory fusion module to capture target variations. The trusted tracking result of the previous frame is used as a dynamic template. The original and dynamic templates are fused using a weight fusion module from the correlated feature perspective to achieve more accurate target localization using the original and short-term memories during the tracking process. The template update mechanism is applied to three mainstream algorithms, SiamRPN, SiamRPN++ and RBO, and experiments are conducted on the VOT2019 public dataset. The results show that the performance of the algorithms is effectively improved after applying the mechanism. Specially, for the SiamRPN++ algorithm, the average overlap expectation is improved by 6.67%, the accuracy is improved by 0.17%, and the robustness is enhanced by 5.39% after applying the template update mechanism. In addition, the SiamRPN++ algorithm with the mechanism has better tracking performance in complex scenarios with occlusion, deformation and background interference.