<p>Accurate classification of thin-section rock images is critical for geological surveys, resource exploration, and automated petrographic analysis. However, existing deep learning models struggle to balance local texture representation and global semantic context, often leading to suboptimal performance in fine-grained rock type recognition. In this study, we propose HFANet, a novel hybrid local–global feature attention network that integrates a DenseNet-based local branch and a Swin Transformer-based global branch to simultaneously capture detailed textures and contextual semantics. A multi-head self-attention module enhances local feature interactions, while a bidirectional cross-attention mechanism enables dynamic guidance between local and global representations. To further improve discriminative capability, we introduce an ensemble classification framework comprising three independent heads with adaptive fusion, and incorporate handcrafted geological features—such as color, texture, and luminance—via multimodal fusion. Extensive experiments on a comprehensive rock thin-section dataset demonstrate that HFANet outperforms a wide range of state-of-the-art models across multiple tasks, achieving up to 99.03% accuracy and perfect AUC and AUPR scores on the sedimentary subset. Ablation studies and interpretability analyses confirm the effectiveness of each component and the model’s ability to focus on geologically meaningful regions. The results highlight HFANet’s strong potential for advancing intelligent geoscientific image analysis and facilitating practical applications in lithological classification.</p>