Compared to coarse-grained image classification, fine-grained image classification tasks exhibit smaller differences between object categories. For species such as birds, which possess a rich diversity of appearance and morphological features, fine-grained bird image recognition has become an important task in the field of computer vision. Additionally, this task is of significant importance for the conservation and research of endangered bird species. In this study, we propose a novel module for fine-grained image classification issues—the Feature Enhancement Fusion and Regularizer Module (FEFR). This module includes Enhancement Fusion Module (EFM) and Feature Regularizer (FR). EFM integrates multi-scale information within the same stage, expanding the model’s capacity to capture context and enhancing the interaction between features at different scales to enrich and complexify feature expressions. FR optimizes the feature space by bringing closer the distances between features of the same category while increasing the separation between different categories, thereby enhancing the model’s robustness to intra-class differences and its ability to recognize inter-class variations. Utilizing MetaFormer as the backbone, our experiments on the CUB-200-2011, NABirds, and Ningxia-Birds datasets demonstrate the efficacy of the FEFR module in fine-grained image classification tasks.