  China Space Station Telescope (CSST) has the capability to conduct slitless
spectroscopic survey simultaneously with photometric survey. The spectroscopic
survey will measure slitless spectra, potentially providing more accurate
estimations of galaxy properties, particularly redshift, compared to broadband
photometry. However, due to low-resolution and signal-to-noise ratio of
slitless spectra, measurement of these properties is significantly challenging.
In this study, we employ a Bayesian neural network (BNN) to assess the accuracy
of redshift estimations from slitless spectra anticipated to be observed by
CSST. The slitless spectra are simulated based on real data from the early data
release of the Dark Energy Spectroscopic Instrument (DESI-EDR) and the 16th
data release of the Baryon Oscillaton Spectroscopic Survey (BOSS-DR16),
combining the 9th data release of the DESI Legacy Survey (DESI LS DR9). The BNN
provides redshifts estimates along with corresponding uncertainties, achieving
an accuracy of $\sigma_{\rm NMAD} = 0.00063$, outlier percentage $\eta=0.92\%$
and weighted mean uncertainty $\bar{E} = 0.00228$. These results successfully
meet the requirement for cosmological studies using slitless spectra from CSST.
