摘要：

Abstract:

【Purpose/significance】This paper studies the Knowledge Graph construction method of the medical question and answer

community. Aiming at the large amount of data, poor standardization and sparse data of the question-and-answer community, this pa⁃

per comprehensively uses the bidirectional long-term memory neural network, conditional random field, bidirectional gated recurrent

unit and other models to study the Entity Recognition and Relation Extraction methods of community text.【Method/process】Firstly,

the entity is further subdivided. The bidirectional long-term memory neural network and the conditional random field model (BiL⁃

STM-CRF) are used to identify the data set of the BIO. The experiment finds that the segmented entity performs better than the

un-subdivided entity. Then the relationship between the entities is extracted by the bidirectional gated recurrent unit and the attention

mechanism model (BiGRU-Attention).【Result/conclusion】The experimental results show that the model has a greater improvement

than the BiLSTM-Attention extraction model in terms of accuracy, recall rate and F value. Finally, a visual Knowledge Graph was con⁃

structed using the Neo4j graph database. This research transforms unstructured community texts into structured data, which promotes

intelligent knowledge services, knowledge representation, and personalized knowledge recommendation in the medical community.

【Innovation/limitation】In the process of medical entity recognition, entities are subdivided, and a breast cancer Knowledge Graph

based on the text of online medical community question and answer is successfully constructed. However, due to the small sample size

of some relationships, there is a certain impact on the evaluation indicators of the overall relationship extraction.