Target tracking usually only uses the appearance information of the first frame of the video to obtain the appearance characteristics of the target online, and predict the position and size of the target in subsequent frames. However, the appearance of the target changes all the time during the tracking process, and the appearance of subsequent targets is not be accurately described by the first frame alone. Focusing on the above problems, this paper proposes a Siamese network target tracking algorithm based on parallel multi-appearance features. First, dynamic template frames containing information about the recent appearance of the target are introduced. At the same time, three methods of multi-appearance, parallel appearance and parallel multi-appearance are proposed, which make dynamic template frames for target tracking. Second, either the evaluation strategy of information entropy or the evaluation method of the neural network in the evaluation module is applied to score the obtained multiple predictions separately, and the prediction result with the highest score is selected as the final prediction result. Finally, an update module is proposed to analyse the scores obtained from the evaluation module. If the score meets the update conditions set by the update module, the final prediction result is used to update the dynamic template frame, and the new appearance information is used to guide the tracking of the next frame. The experimental results show that the algorithm achieves good results on standard datasets such as GOT-10k, OTB100, which verif the effectiveness of the proposed algorithm.