为了推动中医药文本数据的分析和挖掘，实现知识的智能化提取和处理。通过采用BIO（begin, inside, outside）序列标记法、BiLSTM-CRF模型和人工定义规则的方法完成知识抽取任务。利用Python3.6中Py2neo库与Neo4j数据库等技术，构建了基于Neo4j的脾胃病知识图谱，并使用Flask框架开发了中医药脾胃病命名实体识别系统。结果表明：模型（BiLSTM-CRF）在测试集上取得了高性能和良好的泛化能力，其准确率、精确率、查全率和F₁分数分别为96.19%、86.64%、88.82%和87.71%。构建出的知识图谱包含了方剂或中成药、中药、临床表现等8种节点标签以及10种关系类型，可支持中医药治疗脾胃病西医诊断、中医证候、中医治则等节点及各节点之间关系的查询与发现。可见BiLSTM-CRF模型在中医药脾胃病命名实体识别方面展现出了良好的通用性，它在处理复杂文本结构和领域术语上表现出了出色的能力，为中医药脾胃病知识提取和知识图谱构建研究提供了有力支持。

In order to advance the analysis and mining of TCM (traditional Chinese medicine) text data and achieve intelligent extraction and processing of knowledge, the BIO(begin, inside, outside) sequence labeling method, the BiLSTM-CRF model, and manually defined rules were adopted to complete the knowledge extraction task. Utilizing the Py2neo library in Python 3.6 and the Neo4j database, a spleen and stomach disease knowledge graph was constructed based on Neo4j, and a TCM spleen and stomach disease named entity recognition system was developed using the Flask framework. The results show that the BiLSTM-CRF model achieves high performance and good generalization ability on the test set, with accuracy, precision, recall, and F₁ scores of 96.19%, 86.64%, 88.82%, and 87.71%, respectively. The constructed knowledge graph includes eight types of node labels, such as prescriptions or patent medicines, Chinese medicines, and clinical manifestations, as well as ten types of relationships. It supports the querying and discovery of nodes and relationships among Western medical diagnosis, TCM syndromes, and TCM treatment principles for spleen and stomach diseases. It is concluded that the BiLSTM-CRF model demonstrates excellent generalizability in named entity recognition of TCM spleen and stomach disease. It exhibits outstanding performance in handling complex text structures and domain-specific terminology, providing strong support for the research on knowledge extraction and knowledge graph construction in Traditional Chinese Medicine for spleen and stomach diseases.