Objective To explore the application effect of deep learning and migration learning technology based on Faster R-CNN algorithm in local feature extraction and recognition of teeth-printed tongue and fissured tongue in traditional Chinese medicine（ TCM）. Methods The tongue images of 500 subjects were labeled in the areas of teeth-printed tongue and fissured tongue with the LabelImg V 1. 6 software

and the training data set and test data set were divided. The deep learning technique of Faster R-CNN and the fine-tune in the migration learning technology were used to construct the model

and finally the precision rate

the accuracy rate

and the recall rate were used to evaluate the model effect. Results For the fissured tongue

the model identified 99 cases of true positive

3 cases of false negative

45 cases of true negative

3 cases of false positive. The precision rate was 0. 960

the accuracy rate was 0. 971

and the recall rate was 0. 971. For the teeth-printed tongue

the model identified true positive 60 cases

and false negative 20 cases

true negative 69 cases

and false positive 1 case. The precision rate was 0. 860

the accuracy rate was 0. 983

and the recall rate was 0. 750. The model has a macro precision rate of 0. 910

a macro accuracy rate of 0. 977

and a macro recall rate of 0. 860. The image recognition results showed that the model was not affected by the location of the pathological changes in the tongue image

and it also had strong adaptability to the local feature extraction of the tongue image. Conclusion Based on the method of deep learning and migration learning

the local feature recognition task of TCM tongue image can be completed well and has good migration ability.