In order to improve the accuracy of the tracking algorithm when the target is deformed and occluded, a twin target tracking algorithm integrating a HOG(histogram of oriented gradient,HOG) feature and an attention model is proposed . First, the CIR model improved by the ResNet residual model is used to shape the backbone network of a twin target tracking network, which making full use of different levels of feature maps to deepen the network. Secondly, the HOG feature is integrated to enhance the robustness of the network to the geometric changes of graphics. Thirdly, the CBAM(convolutional block attention module) attention model is added to enable the network to adjust the proportion of HOG features in the feature map while combining the context information, enhance the effective features in the feature map, weaken the invalid features, and make each feature map in the network play the best effect. Finally, the loss function of the algorithm is defined. Experimental results show that the proposed algorithm can achieve good tracking effect on OTB100 after training on GOT-10k dataset, and the accuracy and success rates in this dataset are 81.9% and 60.6%, respectively. When the target object is deformed and occluded, the proposed algorithm can still achieve better tracking results.