Purpose/Significance The paper discusses the diagnostic effectiveness of artificial intelligence (AI) auxiliary diagnosis technology applied to lymphatic diseases, expounds the solution of model fine-tuning training under the background of scarce samples, points out the difficulties and shortcomings of applied research, and puts forward prospects. Method/Process Based on the unstructured text data of patients’ electronic medical records (EMR), the ERNIE 3.0 classification model is used to conduct a study on the application of lymphedema auxiliary diagnosis. Through two levels of classification tasks, intelligent diagnosis of lymphedema disease and distinguish between primary lymphedema and secondary lymphedema are realized.Result/Conclusion The ERNIE 3.0 classification model shows accuracy, precision, recall, and mean F1 values over 0.95 in the discriminatory task of lymphedema. The mean Macro F1 value of the model reaches 0.901 in the differentiation of primary and secondary lymphedema. The AUC of the model reaches 0.97 and 0.865 in the two tasks, respectively, indicating the accurate classification effect of the model. In addition, the model classification results have good interpretability, and fill the application gap of intelligent methods in the field of lymphatic diseases.