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In recent years, deep learning technology has made significant progress in the field of target detection and recognition in synthetic aperture radar (SAR) images, yet its application still faces complex challenges. Despite deep neural network models achieving recognition rates exceeding 99% under standard operating conditions on the moving and stationary target acquisition and recognition dataset, the unique imaging mechanisms of SAR, its background dependency, variations in imaging parameters, and diversity in preprocessing lead to highly variable image statistical characteristics, thereby affecting the performance of deep learning models. Dataset bias, particularly the bias induced by different SAR image quantization methods, is one of the key factors impacting the generalization capability of models. This article delves into the impact of quantization methods on neural network performance and analyzes strategies to overcome dataset bias to enhance the stability and generalization capability of SAR image recognition. Experimental results indicate that models trained with adaptive quantization can learn more general features; linear quantization exhibits poor generalization when not enhanced, but this can be improved through data augmentation. Furthermore, pretraining and data augmentation techniques significantly enhance the classification performance of models under different quantization strategies, providing scientific evidence for optimizing SAR imaging system design and constructing reasonable datasets.