  Entity and relationship extraction is a crucial component in natural language
processing tasks such as knowledge graph construction, question answering
system design, and semantic analysis. Most of the information of the Yishui
school of traditional Chinese Medicine (TCM) is stored in the form of
unstructured classical Chinese text. The key information extraction of TCM
texts plays an important role in mining and studying the academic schools of
TCM. In order to solve these problems efficiently using artificial intelligence
methods, this study constructs a word segmentation and entity relationship
extraction model based on conditional random fields under the framework of
natural language processing technology to identify and extract the entity
relationship of traditional Chinese medicine texts, and uses the common
weighting technology of TF-IDF information retrieval and data mining to extract
important key entity information in different ancient books. The dependency
syntactic parser based on neural network is used to analyze the grammatical
relationship between entities in each ancient book article, and it is
represented as a tree structure visualization, which lays the foundation for
the next construction of the knowledge graph of Yishui school and the use of
artificial intelligence methods to carry out the research of TCM academic
schools.
