In order to solve the problem that the existing SAR images target recognition algorithm has low recognition accuracy and low generalization ability, this paper proposes a framework named CMNet based on convolutional neural network for SAR images target recognition model. By designing the feature extraction network for the characteristics of SAR images, introducing center loss in the loss function to construct joint loss with softmax loss for supervision training process, the intra-class aggregation and inter-class separation are considered to improve the accuracy and generalization ability of the algorithm. Batch normalization is introduced after all convolutional layers in CMNet to speed up the convergence of the model and prevent overfitting. The experiments was conducted on Moving and Stationary Target Acquisition and Recognition (MSTAR) benchmark data set. The results show the average recognition rate of the 10 categories of targets reached 99.30%. It is concluded that the proposed CMNet model performs high recognition accuracy and generalization ability and gets promising results on public data sets.