Electronic medical records (EMR) of Traditional Chinese Medicine (TCM) contain rich contents such as chief complaints, subcutaneous symptoms, history of present illness, and past medical history, which are important reference bases for TCM diagnosis. However, there are a lot of terminology and expression irregularities since this information is frequently conveyed in natural language. In this paper, we propose a method to normalize the textual information of EMR of TCM and select the text of medical history with a strong narrative such as the history of present illness and past medical history, as well as the text of symptoms such as chief complaints and subcutaneous symptoms as the main research object. The text is then processed separately according to the type of text. For symptom texts such as chief complaints and subcutaneous symptoms, named entity recognition technology is directly applied to extract symptom entities directly; for medical history texts such as the history of present illness and past medical history, event extraction is performed first to divide the treatment events, and then named entity recognition technology is applied to extract various entities, and finally, the various entities are stored in a database...