This paper proposes a novel method for the automatic diagnosis of keratitis using feature vector quantization and self-attention mechanisms (ADK_FVQSAM). First, high-level features are extracted using the DenseNet121 backbone network, followed by adaptive average pooling to scale the features to a fixed length. Subsequently, product quantization with residuals (PQR) is applied to convert continuous feature vectors into discrete features representations, preserving essential information insensitive to image quality variations. The quantized and original features are concatenated and fed into a self-attention mechanism to capture keratitis-related features. Finally, these enhanced features are classified through a fully connected layer. Experiments on clinical low-quality (LQ) images show that ADK_FVQSAM achieves accuracies of 87.7%, 81.9%, and 89.3% for keratitis, other corneal abnormalities, and normal corneas, respectively. Compared to DenseNet121, Swin transformer, and InceptionResNet, ADK_FVQSAM improves average accuracy by 3.1%, 11.3%, and 15.3%, respectively. These results demonstrate that ADK_FVQSAM significantly enhances the recognition performance of keratitis based on LQ slit-lamp images, offering a practical approach for clinical application.