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Identification of quark flavor is essential for collider experiments inhigh-energy physics, relying on the flavor tagging algorithm. In this study,using a full simulation of the Circular Electron Positron Collider (CEPC), weinvestigated the flavor tagging performance of two different algorithms:ParticleNet, originally developed at CMS, and LCFIPlus, the current flavortagging algorithm employed at CEPC. Compared to LCFIPlus, ParticleNetsignificantly enhances flavor tagging performance, resulting in a significantimprovement in benchmark measurement accuracy, i.e., a 36% improvement for$\nu\bar{\nu}H\to c\bar{c}$ measurement and a 75% improvement for $|V_{cb}|$measurement via W boson decay when CEPC operates as a Higgs factory at thecenter-of-mass energy of 240 GeV and integrated luminosity of 5.6 $ab^{-1}$. Wecompared the performance of ParticleNet and LCFIPlus at different vertexdetector configurations, observing that the inner radius is the most sensitiveparameter, followed by material budget and spatial resolution.

ParticleNet and its application on CEPC Jet Flavor Tagging