In view of the similar object interference, target scale changes, deformation blur, occlusion and other challenging problems for existing deep network tracking algorithms. This paper proposes a robust deep network tracking algorithm by integrating multi-template attention mechanism. The proposed method builds a channel and spatial multi-template attention mechanism in the branch of the Siamfc network, so as to strengthen the ability of the deep network for features extraction, and by integrates shallow and deep convolution features to achieve the accurate focus of tracking targets, so as to overcome the interference problem of similar objects. The adaptive regression network is used to learn the distance between the target sampling point and the target boundary, so as to realize the dynamic prediction of the target area and effectively deal with the problem of target scale change. In addition, the target template online update strategy is established by calculating the APCE mean value and maximum value of classification features, so as to realize the network adaptive the target deformation blur and occlusion problems. Through the test of OTB100, VOT2016 and other public data sets, the results show that compared with the current advanced deep network frameworks such as Siamfc and its improved method, the proposed algorithm has effectively improved the accuracy and success rate of dynamic target tracking, and the research method has a strong robust performance.