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Political trust in China has long been characterized by two prominent features: consistently high levels and a hierarchical pattern where trust in the central government exceeds trust in local authorities. However, existing research overlooks a crucial dimension: the structural interdependency among trust in different political institutions. This study investigates how trust in different levels of government is cognitively organized and whether this internal structure transformed during the COVID-19 pandemic. Drawing on three waves of the Chinese Social Survey (CSS; N=6,726 in 2017, N=8,532 in 2019, N=5,233 in 2021), we employ Belief Network Analysis using Gaussian Graphical Models to examine the relational structure of political trust across nine institutional nodes. Our analysis reveals a fundamental structural transformation. Pre-pandemic, trust in central and rural governments showed negligible association. By 2021, this relationship became significantly negatively correlated (-0.15), with rural governments emerging as critical bottlenecks in the trust network likely due to their frontline pandemic enforcement role. Network intervention analysis examining government performance, social equity, authoritarian values, and political news use shows these determinants shifted from buffering the trust gap pre-pandemic to explaining its widening post-pandemic. Findings suggest COVID-19 transformed Chinese political trust from a static hierarchy into a dynamic, performance-based system where central stability depends on local governments' blame-absorbing capacity, challenging conventional understandings of authoritarian resilience.