Aiming at data imbalance in SAR image recognitiona two-stage transfer learning method based on intra-batch balanced sampling and model fine tuning is proposed. First, the training set of balance data between classes is obtained by using the intra-batch balanced sampling method. Then the training data set is used for pre-training of the model. Finally, the training of imbalanced data and the test is completed by transfer learning and model fine tuning. Aiming at the problem of redundant parameters in the processing of single channel SAR images by using general tri-channel CNN model, a lightweight CNN model is designed for SAR image recognition. Through the three strategies of single-channel convolution kernel, deep separable convolution and using global average pooling instead of full connected layer, the parameters of the model are reduced greatly. The results of experiments conducted on the open data set OpenSARShip show that: 1) The proposed method effectively improves the recognition accuracy of minority classes and reduces the effect of data imbalance on recognition results;and 2) The proposed lightweight CNN model can reduce the size and the single-iteration time of the traditional tri-channel CNN model by about 58.86% and 63.62% respectively, under the premise that the recognition accuracy is basically unchanged.