The SAR-BagNet model is an interpretable deep learning model used for Synthetic Aperture Radar (SAR) image recognition. In order to maintain the interpretability of the SAR-BagNet model while also achieving high recognition accuracy, this paper uses the SAR-BagNet model as a foundation and incorporates spatial attention and coordinate attention mechanisms into the model framework. Experimental results on the MSTAR dataset demonstrate that the spatial attention and coordinate attention mechanisms enhance the SAR-BagNet model's ability to acquire global information. This enhancement effectively improves the model's recognition accuracy and decision rationality without compromising its interpretability.