Based on the improved gravity model, the spatial correlation of local implicit debt in China from 2012 to 2022 was identified, and the social network analysis method and QAP method were used to describe the correlation characteristics, evolution trend and influencing factors of local implicit debt spatial network. The results show that: Local implicit debt presents a spatial correlation network structure with high correlation degree, strong robustness and strict hierarchy, and has obvious spatial spillover effect; the 31 provinces, municipalities and autonomous regions in China can be classified into four sectors, i.e., the main beneficiary sector, the two-way spillover sector, the net beneficiary sector and the broker sector, and there is mutual influence among the four; and geographical adjacency, differences in economic development level, urbanization level and competition among local governments are important factors affecting the spatial correlation network of local implicit debt. Accordingly, each region should formulate implicit debt governance policies applicable to its own region according to its own debt characteristics, and at the same time strengthen exchanges with other regions, formulate cross-regional implicit debt collaborative governance measures, and minimize the occurrence of implicit debt risks and even systemic financial risks.