Abstract: Given the complex network structure of existing regional enterprise association analysis results， which is difficult to comprehend， and the dynamic nature of regional enterprise associations in time and space. In response to the challenges in interpreting results in current regional enterprise analysis， this paper adopts a knowledge graph-based model for regional enterprise association analysis. It utilizes diverse and heterogeneous data for knowledge extraction and storage， coupled with the Neo4j graph database to realize knowledge storage of regional enterprise relationships. In terms of force-directed layout， the utilization of repulsive force optimization and node-edge processing successfully achieves the visualization of enterprise relationships. Through in-depth exploration and analysis of inter-enterprise associations， the aim is to reveal cooperation and competition relationships among regional enterprises， providing decision support for government industrial policy formulation， enterprise investment attraction， and inter-enterprise collaboration. Experimental results demonstrate that the model accurately reveals inter-enterprise relationships， offering robust support for regional economic development.

Key words:  , knowledge graph, enterprise correlation analysis, regional economy, relationship mining, visual decision support