In order to solve the problem that most tracking algorithms based on siamese networks have weak feature extraction ability of backbone networks and template is not suitable for target transformation, this paper proposes the siamese network algorithm based on deformable convolution(DCSiam) on the basis of SiamFC. Firstly, the deformable convolution module is used to learn the adaptive offset of multi-layer feature data in different directions, and the effective receptive field in the convolution process is increased. The final response map is obtained by multi-layer deformable cross-correlation fusion to enhance the capability of deep semantic feature extraction of the backbone network. Finally, an online template updating strategy with high confidence is adopted, the peak sidelobe ratio and the maximum value of the response graph are calculated in every fixed frame as the basis for updating, and the features are fused in a weighted way to update the template. The tracking performance of the proposed algorithm is evaluated using four common benchmark datasets: OTB2013, OTB2015, VOT2016 and VOT2017. The experimental results show that the overall accuracy and success rate of the DCSiam algorithm are increased by 9.5% and 7.5% respectively compared with the baseline on the OTB2015 dataset,which well realizes the target tracking in complex situations and verifies the effectiveness of the proposed algorithm.