Breast cancer pathological images are considered the “gold standard” for clinical diagnosis of breast cancer, but manual diagnosis suffers from inherent drawbacks such as low efficiency and high subjectivity. Computer-aided diagnosis (CAD) systems can provide objective decision support for clinicians by deeply mining multi-level features such as tissue architecture and cytology from pathological images. However, current CAD systems are still challenged by complex background noise and inconsistency in cross-scale feature representation, which hinder the extraction of critical features. Therefore, this paper proposes a key feature dynamic enhancement network for breast cancer pathological image classification (KFDE), in which the channel-spatial feature enhancement module (CSFE) and the multi-scale feature dynamic fusion module (MFDF) serve as the two core components. The CSFE module effectively suppresses background noise and highlights lesion regions through local channel variance analysis and an energy entropy-driven spatial focusing mechanism. The MFDF module employs a heterogeneous multi-branch convolutional architecture to intelligently fuse cross-scale features, addressing the issue of information fragmentation caused by magnification variation. Experiments on the BreakHis dataset demonstrate that KFDE achieves significant performance improvements, with a benign/malignant classification accuracy of 99.74% and an eight-class subtype classification accuracy of 96.35%, significantly outperforming existing mainstream models.