[Objective] This paper builds knowledge graph for business environment to improve the utilization of resources, aiming to discover the internal entity relationship of development factors, and analyze government decision-making. [Methods] We constructed the knowledge graph based on business environment policy of Beijing, and proposed a knowledge extraction method integrating dependency syntax analysis and semantic role annotation. Then, we constructed a combined classifier to identify entity relationship triples, calculate semantic similarity, as well as perform relationship name fusion and alignment. We also designed an experiment to explore the performance of trans R model in different link prediction tasks. Finally, we identified the main influencing factors and used adjustment strategies to complete knowledge reasoning. [Results] The newly constructed knowledge graph contains 31,955 entities, 1,847 relationships and 45,682 triples. The data was stored and visualized with Neo4j and Gephi, which also supported knowledge query using cypher statement. [Limitations] Due to the complex context information, more research is needed to build a model for unclear entities to improve the performance of knowledge extraction and the quality of knowledge graph triples. [Conclusions] Our new knowledge graph could help to build an effective Q&A system, and improve the government decision-making to optimize business environment.