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.

基于条件随机场的分词和实体关系抽取模型的构建及TF-IDF信息检索和数据挖掘的常用加权技术用于提取不同古籍中的重要实体信息，同时使用基于神经网络的依赖句法分析器来分析每个古籍文章中实体之间的语法关系，并以树结构可视化的形式表示，为研究Yishui学派的知识图谱的构建和应用人工智能方法开展中医学术研究奠定基础。

通过文本挖掘和自然语言处理研究构建逸水学派的句法分析图