A UAV target tracking algorithm that combining Siamese network with Transformer are proposed to address the issues of high computational complexity and low tracking accuracy in traditional target tracking algorithms，effectively addressing various challenges during the UAV tracking.Firstly，a backbone network is designed based on the lightweight feature extraction network GhostNet to extract multi-scale features of targets.Secondly，an improved Transformer is employed to construct the multi-scale feature fusion layer，which effectively integrates the multi-scale features of the target.Finally，the improved Transformer is combined with the cross-correlation operation of the Siamese network to integrate global and local information of features，achieving precise localization of the target.The experimental results conducted on the UAV123，GOT-10k，and LaSOT datasets demonstrate that the proposed algorithm exhibits excellent tracking performance with a speed of 87 frames per second，realizing real-time object tracking on both GPU and CPU platforms.