Aimed at the issue of knowledge islands between water conservancy project data texts

this paper presents a dam safety knowledge graph construction method based on the UIE framework and the SE-RE-Joint model. We use a seven-step method to construct a multi-layer domain ontology library of main structure

affiliated buildings

and comprehensive evaluation. After data preprocessing

fine-tuning of small samples is used to realize preliminary entity recognition. We complete entity recognition and relationship extraction through semantic enhanced coding

relying on the bidirectional GRU to capture sequence dependence and CRF to optimize label sequence consistency. And

a Neo4j graph database is used to store knowledge. Test results show that the UIE+SE-RE-Joint model extracts 25

000 entities and 31

000 relational pairs

and achieves a prediction accuracy higher than 84%. The four types of errors－entity conflicts

boundary ambiguities

type confusions

and relationship misjudgments－all are lowered significantly relative to the comparison model. The map supports knowledge visualization and data retrieval; Through Python-based implementation

it completes knowledge dynamic updating

knowledge reasoning

and risk positioning

assisting the intelligent diagnosis in dam safety management.