Research on the Application of Knowledge Graph Technology in Visual Analysis of Contemporary Writers’ Creative Context

Abstract

With the deepening of digital humanities research, traditional studies of writers’ creative works, which mainly rely on close reading and inductive interpretation, face limitations in structural modeling, evolutionary path visualization, and systematic integration of multidimensional semantic relationships. This paper introduces knowledge graph technology to construct a structured modeling and visualization method for the creative trajectory of contemporary Chinese writers. Through multi-source text element extraction and entity-relationship modeling, information such as works, themes, imagery, characters, and creative stages is transformed into structured data. A time-axis-driven graph construction method and multidimensional semantic fusion mechanism are then used to express cross-work and cross-stage relationships. A visualization model based on time paths and theme-imagery networks is designed, and a multi-level interaction mechanism supports dynamic analysis. Application results show that the method effectively reveals stage characteristics and internal structural relationships in writers’ creation. In semantic network analysis, 23 theme nodes and 48 imagery nodes are identified, with an average node degree of 5.91 and a clustering coefficient of 0.63, indicating strong correlations and structural clustering among creative elements.