There are three problems about feature extraction and fusion in the current mainstream tracking framework based on Transformer: 1. The two modules of feature extraction and fusion are used separately, which is easy to produce sub-optimal model training results. 2. Computational complexity of O\left(N^2\right) using self-attention reduces tracking efficiency. 3. The target template selection strategy is simple and is difficult to adapt to the drastic changes in the target appearance during the tracking process. We propose a novel Transformer tracking framework using fast Fourier transform mixing target tokens and search region tokens. For problem 1, an efficient end-to-end approach is proposed to extract and fuse features for unified learning to obtain optimal model; For problem 2, the fast Fourier transform is used to achieve complete information interaction between the target tokens and search region tokens. The computational complexity of this operation is O\left(N\mathrml\mathrmo\mathrmg\left(N\right)\right) , which greatly improves the tracing efficiency. For problem 3, a template memory storage mechanism based on quality assessment is proposed, which can quickly adapt to the drastic changes in target appearance. Compared with the current state-of-the-art algorithms on three datasets LaSOT, OTB100 and UAV123, our tracker achieves better performance in both efficiency and accuracy.