The siamese network tracking algorithm can turn tracking problem into similarity matching problem,which has attracted widespread attention.However,most algorithms based on siamese network cannot be applied to devices such as mobile terminals or embedded devices with insufficient computing power.Thus,a lightweight,high-speed tracking algorithm based on siamese network is proposed.The algorithm deploys MobileNetV2 as the backbone network,which has good feature extraction capabilities and less parameters,and the network parameters are further reduced by group convolution,Crop and other operations to improve the network operation speed.SE module is added to the inverted residual structure to dynamically adjust the weight of the model,highlight important information.Through the fusion of information of different layers,the expression of the target multi-scale semantic information is improved. The results of the target tracking benchmark library OTB100 and VOT2018 show that,compared with the existing general tracking algorithms,the proposed algorithm not only maintains high accuracy,but also runs at a speed of 170 frames per second,which has a good engineering application prospect.