In the field of fine-grained image classification of Chinese herbal medicine， the lack of a comprehensive and balanced dataset has been a major obstacle. To advance research on fine-grained image recognition of Chinese herbal medicine， a Herb-150 fine-grained Chinese herbal medicine dataset was constructed， with balanced sample distribution and comparable counts per category. To address the issue of deep neural networks easily losing discriminative， detailed features in this task， a fine-grained feature-enhanced CHMRN （Chinese Herbal Medicine Recognition Network） was proposed. By introducing a top-down feature fusion module， it integrated multi-scale semantic information to capture comprehensive contextual features. Additionally， a bottom-up channel feature information compensation module was designed to enhance the expressive power of fine-grained features， ensuring the accurate capture of subtle differences among traditional Chinese medicine categories. Experimental results showed that CHMRN achieved an accuracy of 93.910% on the Herb-150 dataset， outperforming mainstream models such as CMAL-Net （Cross-layer Mutual Attention Learning Network）， validating its effectiveness in fine-grained classification tasks. The CHMRN not only improves the accuracy of traditional Chinese medicine identification， but also provides valuable references for similar fine-grained image classification applications.