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.

The semantic analysis of modal verbs faces many challenges due to their inherent complexity， requiring the extraction of diverse feature sets for Word Sense Disambiguation （WSD）， including semantic， syntactic， pragmatic， and genre features. These features vary in the contribution to semantic disambiguation， and some are confusing or redundant. To eliminate the influence of confusing and redundant features on WSD， a word sense disambiguation approach based on Causal Partial Order Diagram （CPOD） was proposed. Learning from the concept of intervention in causal reasoning， eliminating confusing factors through do-calculus， and combining with the idea of constructing an Attribute Partial Order Diagram （APOD）， a CPOD was developed as a model for WSD of modal verbs. Results of WSD experiments on 15 English modal verbs showed that the proposed method achieved an average accuracy of 93.42%， eliminating confusing and redundant features. Furthermore， to quantify the specific contribution of each feature to WSD， each feature's contribution was calculated and ranked. It is found that semantic features contribute the most， followed by syntactic features， while genre features contribute relatively less. Specifically， in semantic features， features with low mutual information contribute much more to WSD than those with high mutual information， making them the most influential features.