Most of the existing synthetic aperture radar(SAR) automatic target recognition(ATR) algorithm are based on data-driven. However, there is not enough data for some specific target recognition to be trained. In this paper, a siamese network with parameter sharing is created, and then the simulated and real SAR images of the available targets are used as sample pair inputs, and labeled as positive and negative sample pairs based on whether the input sample pairs are of the same category, so that the network can be trained to extract domain invariant features, and then use classifiers to achieve the recognition task of non-homologous targets. The proposed method is validated on the moving and stationary target acquisition and recognition (MSTAR) dataset, The results are that the accuracy of on trained by ten pairs simulation and real SAR images are higher 93.92%, and the accuracy trained by on only one reaches 81.17%.