[Objective] The water poverty in China’s provinces (cities) was measured and social network analysis was conducted in order to provide a decision-making basis for alleviating China’s water poverty dilemma. [Methods] The entropy weight method and social network analysis were employed to measure and analyze the spatial correlation effects of water poverty in various Chinese provinces (cities) from 2010 to 2021. [Results] ① The water poverty index of Chinese provinces has shown an overall upward trend during the inspection period, and the degree of water poverty has gradually decreased. However, the spatial non-equilibrium characteristics remain quite evident. ② The provincial water poverty network exhibits significant spatial correlation and complex structural forms as a whole, with all regions interconnected; however, the degree of closeness of this correlation is not high. ③ According to the block model analysis results, 5 provinces (cities) including Beijing and Tianjin are classified as the “net beneficiary” group, 13 provinces (cities) including Hunan and Hainan as the “net spillover” group, 4 provinces (cities) including Guangdong and Chongqing as the “two-way spillover” group, and 9 provinces (cities) including Inner Mongolia and Heilongjiang as the “broker” group. In addition, the relationships within the plates are sparse, whereas the connections between the plates are strong. ④ Analysis of the core-edge density shows that the number of core areas fluctuates frequently, while the number of edge areas initially increases and then decreases. [Conclusion] The State should develop a comprehensive understanding of the spatial correlation and network structure characteristics of the Water Poverty Index, formulate and implement regionally differentiated policies and strategies, promote coordinated development, give full play to the roles of the government and market, effectively address the challenges of water poverty, and enhance water security.