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In modern maritime activities, the quality of ship communication directly impacts the safety, efficiency, and economic viability of ship operations. Therefore, predicting and analyzing ship communication status has become a crucial task to ensure the smooth operation of ships. Currently, ship communication status analysis heavily relies on large-scale, multi-source heterogeneous data with spatio-temporal and multi-modal features, which presents challenges for ship communication quality prediction tasks. To address this issue, this paper constructs a multi-modal spatio-temporal ontology and a multi-modal spatio-temporal knowledge graph for ship communication, guided by existing ontologies and domain knowledge. This approach effectively integrates multi-modal spatio-temporal data, providing support for subsequent efficient data analysis and applications. Taking the scenario of fishing vessel communication activities as an example, the query tasks for ship communication knowledge are successfully performed using a graph database, and we combine the spatio-temporal knowledge graph with graph convolutional neural network technology to achieve real-time communication quality prediction for fishing vessels, further validating the practical value of the multi-modal spatio-temporal knowledge graph.