This paper introduces a novel plug-and-play Multi-scale Feature Spatial Attention Fusion (MFSAF) module, aiming at enhancing the capabilities of convolutional neural networks (CNNs) in Synthetic Aperture Radar (SAR) ship classification tasks. The MFSAF module integrates spatial attention mechanisms and feature alignment strategies, providing a seamless integration into general CNNs to better capture ship features of different scales. The experimental results on the OpenSARShip2.0 and FUSARShip datasets demonstrate a significant improvement of the "baseline+MFSAF" model compared to baseline model, highlighting the effectiveness of the MFSAF module in capturing SAR ship features and its adaptability across different networks.