In view of the issue of model performance degradation caused by cross-domain transfer in fewshot learning, a lightweight adaptation strategy for few-shot SAR target recognition named SAR-LAM is proposed. This method is to utilize knowledge distillation for pre-training a generalized encoder and embedding an adaptation module trained only with very few target domain samples. The extracted features are then mapped into a more discriminative space, and finally, the query set samples are classified by taking a prototypical network as the baseline. This adaptation strategy is to increase at a few cost in learning parameters, and by so doing, the limitations of model transfer caused by data distribution differences is overcome, improving the model’s ability to extract features in the target domain, and simultaneously improving the accuracy of SAR target recognition by at least 1.93 percentage points under few-shot conditions. And this adaptation strategy is superior in performance to the other methods.