Objective To construct a traditional Chinese medicine(TCM)knowledge base using knowl-edge graph based on deep learning methods,and to explore the application of joint models in intelligent question answering systems for TCM. Methods Textbooks Prescriptions of Chinese Materia Medica and Chinese Materia Medicawere applied to construct a comprehensive knowledge graph serving as the founda-tion for the intelligent question answering system.In the study,a BERT+Slot-Gated(BSG)deep learning model was applied for the identification of TCM entities and question inten-tions presented by users in their questions.Answers retrieved from the knowledge graph based on the identified entities and intentions were then returned to the user.The Flask framework and BSG model were utilized to develop the intelligent question answering sys-tem of TCM. Results A TCM knowledge map encompassing 3 149 entities and 6 891 relational triples based on the prescriptions and Chinese materia medica was drawn.In the question answer-ing test assisted by a question corpus,the F1 value for recognizing entities when answering 20 types of TCM questions was 0.996 9,and the accuracy rate for identifying intentions was 99.75%.This indicates that the system is both feasible and practical.Users can interact with the system through the WeChat Official Account platform. Conclusion The BSG model proposed in this paper achieved good results in experiments by increasing the vector dimension,indicating the effectiveness of the joint model method and providing new research ideas for the implementation of intelligent question answering sys-tems in TCM.