Classification of ships in synthetic aperture radar (SAR) images plays a critical role in maritime search and rescue as well as national defense security. Over the past few years, the swift advancement rapid of deep learning has opened up innovative technological directions for precise classification of SAR ship images. However, the unique imaging mechanism of SAR images, resulting in a lack of ship samples and insufficient feature information, continues to make accurate classification of SAR ships challenging. In this study, we propose an improved vision transformer (ViT) method using a full attention (FA) mechanism, incorporating channel attention after Self-Attention, dynamically selecting channels through reweighting to enhance comprehensive feature representation. To address the issue of insufficient training samples, we designed an image reconstruction module based on SRGAN, which generates high-resolution and detailed image data through adversarial learning, effectively compensating for the sample quantity in the dataset. Ultimately, the results section demonstrates that the method introduced delivers enhanced classification accuracy relative to current methods.