<p>In the era of industrial transformation and intelligent manufacturing, ensuring the surface quality of metal materials is crucial for maintaining component performance and system reliability. Although deep learning-based visual inspection has shown promise in automating defect detection, existing methods heavily rely on large-scale labeled data, which are often scarce and costly to obtain in industrial settings. Furthermore, the high inter-class similarity and variability of metal surface textures pose additional challenges to robust defect classification under low-data regimes. To address these issues, we propose an attention-guided few-shot learning framework specifically tailored for metal surface defect classification. Our method comprises two key components. First, we design a Dual-Branch Attention Module to enhance feature extraction by explicitly modeling both channel-wise dependencies and spatial saliency. This module leverages lightweight convolutional operations to highlight discriminative regions and mitigate feature degradation due to limited training data. Second, we introduce a Cross-Set Guided Attention mechanism to improve semantic alignment between support and query samples. By employing scaled dot-product attention, the model dynamically adjusts feature representations based on cross-sample correlations, thereby enabling fine-grained discrimination of visually similar defect types. Extensive experiments conducted on benchmark metal defect datasets demonstrate that our framework significantly outperforms existing few-shot learning baselines in both classification accuracy and generalization capability. The proposed method provides a practical and efficient solution for real-time industrial quality inspection in data-scarce scenarios.</p>