  The forthcoming CSST wide-field multiband imaging survey will produce
seven-band photometric spectral energy distributions (SEDs) for billions of
galaxies. The effective extraction of astronomical information from these
massive datasets of SEDs relies on the techniques of both SED synthesis (or
modeling) and analysis (or fitting). We evaluate the performance of the latest
version of BayeSED code combined with SED models with increasing complexity for
simultaneously determining the photometric redshifts and stellar population
parameters of galaxies in this survey. By using an empirical statistics-based
mock galaxy sample without SED modeling errors, we show finding that the random
observational errors in photometries are more important sources of errors than
the parameter degeneracies and Bayesian analysis method and tool. By using a
Horizon-AGN hydrodynamical simulation-based mock galaxy sample with SED
modeling errors about the star formation histories (SFHs) and dust attenuation
laws (DALs), the simple typical assumptions lead to significantly worse
parameter estimation with CSST photometries only. The SED models with more
flexible (or complicated) forms of SFH/DAL do not necessarily lead to better
estimation of redshift and stellar population parameters. We discuss the
selection of the best SED model by means of Bayesian model comparison in
different surveys. Our results reveal that the Bayesian model comparison with
Bayesian evidence may favor SED models with different complexities when using
photometries from different surveys. Meanwhile, the SED model with the largest
Bayesian evidence tends to give the best performance of parameter estimation,
which is more clear for photometries with larger discriminative power.
