为提高水·运输安全监管能力，向海事行政执法人员提供有效的信息技术支持，针对智慧海事场景下船舶现场监督知识分散复杂、查询困难的问题，提出了自顶向下与自底向上相结合的知识图谱构建方法。通过分析船舶现场监督三元组结构并构建本体模型，采用基于序列标注模型、爬虫技术进行实体识别与知识抽取，并通过二元分类模型融合领域知识，利用图数据库Neo4j进行知识存储与可视化。以盐城市大丰港为例，构建船舶现场监督知识图谱并进行实验验证。结果表明：该构建方法具有较高的准确度，知识图谱技术可有效关联船舶现场监督知识，有力支持海事执法人员对检查事件的回溯及利用，为实现智慧海事提供了新的方法。

In order to improve the safety supervision ability of waterway transportation, and provide effective information technology support to maritime administrative law enforcement officers, a top-down and bottom-up knowledge graph construction method was proposed to solve the problems of decentralized and complex knowledge and difficult query of vessel on-site supervision in the smart maritime scene. By analyzing the triple structure of vessel onsite supervision and building an ontology model, the entity recognition and knowledge extraction were carried out by using sequence annotation based model and crawler technology, and domain knowledge was integrated by using binary classification model, and knowledge storage and visualization were carried out by using graph database Neo4j. Taking Dafeng port in Yancheng city as an example, the knowledge graph of vessel on-site supervision was constructed and experimentally verified. The results show that the construction method has higher accuracy, the knowledge graph technology can effectively correlate with the vessel on-site supervision knowledge, strongly support the maritime law enforcement personnel to backtrack and use the inspection events, and provide a new method for realizing intelligent maritime.