Research on intelligent bird image classification has significant application value in biodiversity conservation, agriculture, and aviation. Traditional coarse classification techniques are inadequate due to the vast number of bird species and their highly similar appearances. Fine-grained bird image classification technology is more suitable for these scenarios. However, the datasets for this task feature large intra-class differences and small inter-class differences, significantly increasing the difficulty of achieving accurate classification. To address this challenge, we propose a Multi-level Entropy-guided Feature Fusion model (MEFF) based on the Vision Transformer (ViT). Experimental results indicate that our model demonstrates state-of-the-art performance on the fine-grained bird image datasets CUB-200-2011 and NABirds, while also showing strong competitiveness on other more challenging ultra-fine-grained datasets Cotton and SoyLoc, fully validating the model’s generality.