Data-driven network analysis methods provide new perspectives for the understanding of emission reduction synergy. This paper sorts out the historical logic, spatial characteristics and driving factors of China-style modernization regional emission reduction policy synergy from both historical and practical perspectives, in order to provide reference for policy formulation. Different from the existing investigation of the synergy between government departments, this paper constructs an inter-provincial policy network measurement model based on natural language processing, based on big data text analysis to measure the coordination degree of 4988 inter-provincial and regional policies and 275 central policy concerns, introduces social network analysis technology to construct an inter-provincial policy coordination network, and studies the impact of central policy focus on local emission reduction policy coordination. Based on the BERTopic model, it is found that the regional coordinated emission reduction policy has gone through three stages: radiation in key demonstration areas, regional coordinated strategic guidance and national systematic classification policy, and three mechanisms have been formed: differentiated target response, expanded synergy scope and concrete policy content. Applied social network analysis shows that there are 457 emission reduction synergies coexisting among 31 provinces, and the network shows a tight imbalance between the central and eastern parts and the loose imbalance in the western and northern parts of the south. Among them, Beijing, Shanghai and Jiangsu are the core actors, Jilin, Ningxia and Heilongjiang are the marginal actors, and Hubei and Hunan play the role of "bridges"; Finally, based on the secondary assignment procedure of multiple regression, it is found that the higher the attention of the central government, the closer the geographical location, and the greater the gap in economic development level, the more conducive to regional emission reduction coordination. The central government should balance policy concerns, adhere to quantitative targets and structural optimization, and implement policies according to role positioning.