Synthetic Aperture Radar (SAR) imaging under sparse constraint can effectively obtain useful information of the target's distinctive points by enhancing the sparse features with the sparse prior representation. However, this process cannot recover the structure feature of the target, and it is very sensitive to inevitable non-systematic errors. To this end, this paper proposes a sparse recovery high-resolution SAR imaging algorithm for Structure feature Enhancement based on Alternating Direction Method of Multipliers (ADMM) method (SE-ADMM). The algorithm introduces Total Variation (TV) regular term to characterize structural features and play a role in enhancing the structure, introduces ?1 norm to represent sparse features, which can suppress noise, and the entropy norm is introduced to characterize the focusing feature to ensure that the algorithm is insensitive to non-systematic errors. Under the framework of ADMM multi-feature optimization, the "Local-Global" operation mechanism is used to first derive the proximal operators of the three features respectively to obtain the corresponding feature analytical solutions, and then perform the target global optimization to ensure the coordination and balance between the feature solutions. In addition, the reference of multi-splitting variables and multi- regular under the ADMM multi-task framework ensures the efficiency and robustness of the algorithm. In the experimental part, the simulation data and measured data of SAR are selected successively to verify the effectiveness of the algorithm. The recovery performance of the proposed algorithm was quantitatively analyzed through phase transition analysis, and the robustness and advantages of SE-ADMM algorithm proposed in this paper are further verified.