  Jet flavor tagging plays a crucial role in the measurement of relative
partial decay widths of $Z$ boson, denoted as $R_b$($R_c$), which is considered
as a fundamental test of the Standard Model and sensitive probe to new physics.
In this study, a Deep Learning algorithm, ParticleNet, is employed to enhance
the performance of jet flavor tagging. The combined efficiency and purity of
$c$-tagging is improved by more than 50\% compared to the Circular Electron
Positron Collider (CEPC) baseline software. In order to measure $R_b$($R_c$)
with this new flavor tagging approach, we have adopted the double-tagging
method. The precision of $R_b$($R_c$) is improved significantly, in particular
to $R_c$, which has seen a reduction in statistical uncertainty by 40\%.
