As a crucial channel for government financing and a key tool for macroeconomic regulation

the operational efficiency of the treasury bond auction mechanism directly impacts the allocation effect of fiscal resources

the stability of financial markets

and investor confidence. Optimizing the treasury bond auction system to adapt to dynamic market environments

enhance government debt management capabilities

and mitigate potential risks has become an important topic in fiscal policy research. This paper systematically explores the operational mechanisms of the Dutch auction

American auction

hybrid auction

and Chinese-style auction models from the perspective of dynamic market characteristics. It also evaluates the limitations of the current system in responding to market fluctuations through empirical analysis and theoretical modeling. The study finds that dynamic factors have a significant impact on the volatility of auction revenues and market transmission effects. A comprehensive optimization scheme combining the LSTM neural network and the GARCH model demonstrates obvious advantages in balancing fiscal revenue and market stability.