Intelligent Government Media Services Driven by the Integration of Semantic Graph and Context Awareness

Government media is a crucial component in the construction of digital governments, and its service efficiency directly impacts public experience and the modernization level of governance. However, existing services generally suffer from issues such as information overload, slow response, and insufficient personalization, making it difficult to meet the demands for precise and scenario-specific services. Existing studies primarily proceed along two paths: semantic graphs and context awareness. The former focuses on structuring government knowledge to support semantic understanding, while the latter aims to capture user context to enhance service relevance. Nevertheless, these two paths are mostly explored in isolation, with insufficient in-depth research on deep integration mechanisms. This leads to inadequacies in both service precision and proactivity. To address the above problems, this paper proposes a new model of intelligent government media services driven by the integration of semantic graphs and context awareness. Theoretically, it clarifies the integration mechanism of “dynamicizing static knowledge and contextualizing global knowledge”. Technically, a hierarchical intelligent service architecture is designed, integrating a graph neural network (GNN)-based graph representation learning algorithm and an attention mechanism-based context fusion model. This enables in-depth understanding of user intentions and accurate service matching. Through the development of a prototype system and empirical analysis in government consulting scenarios, the results show that the proposed model significantly outperforms traditional methods in key indicators such as response accuracy (P@1) and user satisfaction, verifying its effectiveness and superiority. This study provides theoretical support and practical pathways for the intelligent upgrading of government services.