Background: Museum artwork recognition presents distinct challenges: extreme data scarcity (typically one training image per title), museum-specific imaging conditions, and high intra-class visual variability that requires sensitivity to artistic style rather than semantic content alone. Methods: We present a Style-Aware Hierarchical Recognition Framework that addresses these challenges through three coordinated components. First, a domain-specific augmentation pipeline generates ten synthetic variants per original image, simulating spot lighting, lens distortion, motion blur, and partial occlusion to enrich training diversity. Second, the Style-Aware Feature Fusion (SAFF) module is mounted atop an Inception ResNetV2 backbone; it extracts a 256-dimensional global style embedding via average pooling, generates channel-wise attention weights through a sigmoid-activated dense layer, and reweights the multi-scale feature map in a residual manner to emphasize stylistically discriminative cues such as brushstroke texture, color layering, and compositional rhythm. Third, a two-stage hierarchical classifier first assigns each artwork to one of four departments, then applies a department-specific title-level classifier, reducing the effective classification space from 261 to 42–86 classes per model. Results: On the Metropolitan Museum of Art benchmark, the framework achieves 86% department-level accuracy (F1 = 0.86) and 84% weighted-average Top-1 accuracy across 261 title classes (F1 = 0.81). The end-to-end hierarchical pipeline yields 75% final Top-1 accuracy, substantially outperforming flat baselines including InceptionResNetV2 alone (56%), InceptionV3 (45%), and VGG16 (37%). Ablation analysis confirms that the style embedding is the dominant performance factor (?9% when removed), and SAFF outperforms established attention mechanisms (SE-Net, CBAM, and transformer self-attention) under identical training conditions. Conclusion: Combining domain-aware augmentation, style-sensitive feature fusion, and hierarchical decomposition provides a robust and scalable foundation for museum artwork recognition under extreme data constraints, with direct applicability to accessible museum guidance systems.