Breast cancer is the leading malignant tumor worldwide, and early diagnosis is crucial for effective treatment. Computer-aided diagnostic models based on deep learning have significantly improved the accuracy and efficiency of medical diagnosis. However, tumor edge features are critical information for determining benign and malignant, but existing methods underutilize tumor edge information, which limits the ability of early diagnosis. To enhance the study of breast lesion features, we propose the enhanced edge feature learning network (EEFL-Net) for mammogram classification. EEFL-Net enhances the learning of pathology features through the Sobel edge detection module and edge detail enhancement module (EDEM). The Sobel edge detection module performs processing to identify and enhance the key edge information. The image then enters the EDEM to fine-tune the processing further and enhance the detailed features, thus improving the classification results. Experiments on two public datasets (INbreast and CBIS-DDSM) show that EEFL-Net performs better than previous advanced mammography image classification methods.