<p>Traditional Chinese medicine (TCM) electronic medical record (EMR) is the core carrier to record the patient’s diagnosis and treatment process and TCM discursive thinking, and its Nested Named Entity Recognition (Nested NER) is of great significance for constructing TCM knowledge graph and intelligent application. However, existing methods face three major challenges: insufficient model generalization ability due to the scarcity of TCM text annotation data, catastrophic forgetting problem in domain adaptation of large language models, and semantic illusion phenomenon of generative models in nested entity recognition. To address the above problems, this study proposes a three-stage optimization framework approach, KDC-NER: first, multi-dimensional data augmentation of self-constructed TCM electronic medical record dataset based on an improved EDA method, and expanding the domain corpus through strategies such as synonym substitution, entity replacement, and so on; second, designing a dynamic data filtering mechanism for knowledge augmentation in TCM, combining the entity distribution a priori with semantic similarity computation, to alleviate the big model knowledge forgetting problem in fine-tuning; finally, a constraint generation method based on cue engineering is proposed to suppress non-relevant entity illusions in the generation process through entity boundary-aware templates and knowledge verification modules. The experimental results show that the proposed method achieves 80.57% F1 value in the self-constructed TCM electronic medical record dataset, which is improved compared with the traditional BERT-BiLSTM-CRF, GP, and GPT-3.5-turbo baseline models, and verifies the effectiveness of the data augmentation, dynamic filtering and constraint generation strategies. This study provides a scalable solution for nested entity recognition in the TCM domain and offers new ideas for large model domain adaptation research in low-resource scenarios.</p>