Against the backdrop of the vigorous development of artificial intelligence and big data technologies， knowledge graphs， which integrate multidisciplinary knowledge， have achieved remarkable results in fisheries and many other professional fields. Relevant research is conducive to addressing issues such as the fragmentation of professional knowledge and insufficient visualization. Therefore， this paper systematically sorts out the basic theories of knowledge graphs， including key technologies such as construction methods and processes， ontology construction， and knowledge extraction， as well as the application progress and challenges in the fishery field. Meanwhile， this paper explores the research hotspots， specific application situations and existing deficiencies in this field based on bibliometric methods. The research indicates that while the application of fisheries knowledge graphs demonstrates a growing adoption and has achieved progress in core domains such as bibliometric analysis， information retrieval， intelligent Q&A， and recommendation systems， it still faces widespread limitations. These include a narrow research scope， outdated retrieval algorithms， inadequate complex reasoning capabilities， and ineffective handling of long-tail content. Furthermore， although knowledge graph construction is transitioning from manual to automated processes， with technologies for ontology development， knowledge extraction， and storage maturing， significant shortcomings persist in knowledge fusion， reasoning， and visualization， posing challenges such as data heterogeneity and domain complexity. The research suggests that the future application of knowledge graphs in fisheries should focus on areas such as data integration and standardization， construction of multimodal fishery knowledge graphs， and graph neural network prediction based on fishery knowledge graphs， so as to promote the intelligent development and sustainable development of fisheries.