The automatic classification of histopathological images plays a crucial role in cancer diagnosis. However, most existing medical image classification studies based on Capsule Network (CapsNet) suffer from issues such as overly localized feature extraction, redundancy in low-level capsule information within routing mechanisms, and inadequate learning of category-specific features for multiclass tasks. This work proposes a novel Dual-Resolution Attention Capsule Network (DRA-CN) tailored for histopathological image classification, which successfully achieves precise classification. During the image feature learning step, DRA-CN introduces a dynamic routing optimization strategy for capsule features and a dual-resolution attention feature fusion strategy to enhance the network’s capability to capture image information. To enhance category learning, DRA-CN integrates a category attention block model to enhance classification performance. Experimental results demonstrate the superior performance of DRA-CN in histopathological image classification tasks, outperforming existing models. On the ChaoYang multi-class dataset, DRA-CN achieved a classification accuracy of 97.50 \(\%\) , F1-Score of 84.26 \(\%\) , precision of 84.54 \(\%\) , and recall of 83.98 \(\%\) .