Traditional Siamese network target tracking algorithms use cross-correlation or deep cross-correlation to calculate the similarity measurement between template frame and detection frame, which can't effectively adapt to extreme target deformation. In this paper, an IoU-aware target tracking algorithm based on graph network is designed based on the target tracking algorithm without anchor point frame. Firstly, based on Resnet50, the normalization-based channel attention module(NCAM) is introduced after each residual structure to construct a lightweight and efficient feature extraction network with channel adaptive. Then, a new similarity calculation method of template frame and detection frame is designed based on the graph network. The pixel of the feature map is regarded as the node of the graph network, and the similarity calculation is carried out on the node of the graph network with template feature and detection feature to effectively deal with the extreme deformation of the target. Finally, in the classification and regression part, the IoU-perceived classification loss function is used in the classification part to establish a connection between the classification branch and the regression branch, which changes the inconsistency between the training and testing of the Siamese network target tracking algorithm in the past. In the regression part, CIoU loss is used to calculate the regression loss in the off-line training stage, and a more accurate boundary box is obtained. Experimental results on the OTB2015, UAV123, VOT2018 and VOT2019 data sets demonstrate the effectiveness of the proposed algorithm.