In view of the requirements of target recognition, the deep learning methods based on neural network are set off. Generally, there is a priori hypothesis of data contained in the indepth learning model, the artificial design aimed at neural network for data needs an abundance of priori knowledge for experts in the field, and has the disadvantages of laborintensive and high time cost. In order to obtain better network performance beyond the personal experience of network design experts, an efficient structure search method, i.e. Differentiable Architecture Search, is adopted. In this method, the search space is broadened to be continuous, and then the performance of the verification set of the architecture is optimized by gradient descent, finding the optimal neural network structure for target recognition. The simulation results show that it is feasible to apply the target recognition method based on neural network structure search to the LSS target recognition.