Breast cancer whole slide image classification is critical for accurate diagnosis. However, existing pseudo-label-based multiple instance learning methods suffer from low-quality pseudo-labels and suboptimal selection of hard negative instance ratios. To address these issues, this paper proposes a multiple instance learning method combining frequency domain features and dynamic hard negative instance screening. First, a multi-scale frequency domain feature encoding module is designed, which enhances high-frequency details and complex texture representations through frequency domain residual connections and cross-layer feature fusion. Second, a dual-branch bag prediction module is proposed to dynamically adjust instance weights via an attention mechanism, mitigating feature dilution caused by heterogeneity and improving pseudo-label generation quality. Finally, a dynamic hard negative instance pseudo-label mining strategy is introduced, progressively increasing the proportion of hard negative instances to enhance the model’s ability to capture discriminative features. Experimental results on the Camelyon and TCGA-BRCA datasets demonstrate significant improvements: ACC, AUC, Precision, and Recall increased by 3.15%、1.72%、3.06%、2.12% and 2.32%、2.79%、2.22%、2.22%, respectively. These advancements validate the effectiveness of the proposed method.