To explore the construction of word segmentation model suitable for the field of traditional Chinese medicine (TCM).Methods:Using the unsupervised learning word segmentation method based on SentencePiece，we proposed to use 3 different types of documents，such as published textbooks，famous works and clinical medical records of TCM，to construct a word segmentation model of TCM; choosed the clinical records of TCM and medical records of famous doctors as the test set for model testing.Results:The Kappa coefficient of the word segmentation model of TCM established in this study was 0.79 (with substantial consistency)，the accuracy rate was 0.84，the macro precision rate was 0.84，the macro recall rate was 0.83，and the macro f1 score was 0.83.Conclusion:The word segmentation model constructed by this study has a good segmentation effect on the terminology of TCM，indicating that this method can be applied to the construction of the word segmentation model in the field of TCM，and can provide a methodological reference for further study of TCM word segmentation.