Whole slide images （WSIs） serve as the golden standard for pathological diagnosis， and their accurate classification provides critical information on tumor type， grade， and stage， which is essential for cancer prognosis and treatment strategy selection. In computational pathology， multi-instance learning （MIL） has become the mainstream approach for WSI classification. However， most existing MIL methods focus on single-scale pathological images， limiting the understanding of cancer development and progression mechanisms across different levels. Additionally， the high resolution of WSIs and information discrepancies across scales pose challenges to efficiently integrating and analyzing patches both within a single scale and across multiple scales. To address these issues， this paper proposes a WSI classification method based on deformable attention and multi-scale multi-instance learning （DMSMIL）. Specifically， a deformable attention branch is designed to learn associations among patches within the same scale， enhancing attention computation efficiency. Meanwhile， an optimal transport （OT）-based association algorithm is developed to integrate pathological information across different scales， enabling efficient multi-scale information alignment. Experimental results on breast cancer and lung cancer subtype classification tasks demonstrate that the proposed method achieves classification accuracies of 85.39% and 92.00%， respectively， outperforming mainstream WSI classification methods. The proposed DMSMIL effectively integrates multi-scale pathological features and improves the accuracy of WSI-based cancer subtype classification， providing a promising approach for computational pathological diagnosis.