Significance The paper discusses the application of artificial intelligence technology to the key entity recognition of unstructured text data in the electronic medical records of lymphedema patients. Method/Process It expounds the solution of model fine-tuning training under the background of sample scarcity, a total of 594 patients admitted to the department of lymphatic surgery of Beijing Shijitan Hospital,Capital Medical University are selected as the research objects. The prediction layer of the GlobalPointer model is fine-tuned according to 15 key entity categories labeled by clinicians, nested and non-nested key entities are identified with its global pointer. The accuracy of the experimental results and the feasibility of clinical application are analyzed.Result/Conclusion After fine-tuning, the average accuracy rate, recall rate and Macro_F1 of the model are 0.795,0.641 and 0.697, respectively, which lay a foundation for accurate mining of lymphedema EMR data.