Abstract: The study of pulmonary nodule pathology using manual reading and analysis of pulmonary nodule medical records is prone to case entity omission and inefficient feature extraction. Based on the ERNIE 2.0 model, medical research valuable entities such as diseases, abnormal detection results, and diameters in pulmonary nodule medical records were extracted and processed into structured text for doctors to conduct relevant searches, statistics, and research. The experiments show that the proposed model has the advantages of deep dissection of knowledge to enhance semantic capability and a richer corpus, and can understand relatively complex semantics with certain generalization. With F1 value up to 94%, it has significant improvement in effectiveness than BiLSTM and BERT models.