  Modern large-scale photometric surveys have provided us with multi-band
photometries of billions of stars. Determining the stellar atmospheric
parameters, such as the effective temperature (\teff) and metallicities (\feh),
absolute magnitudes ($M_{G}$), distances ($d$) and reddening values (\ebr) is
fundamental to study the stellar populations, structure, kinematics and
chemistry of the Galaxy. This work constructed an empirical stellar library
which maps the stellar parameters to multi-band photometries from a dataset
with Gaia parallaxes, LAMOST atmospheric parameters, and optical to
near-infrared photometry from several photometric surveys. Based on the stellar
library, we developed a new algorithm, SPar (\textbf{S}tellar
\textbf{P}arameters from multib\textbf{a}nd photomet\textbf{r}y), which fits
the multi-band stellar photometries to derive the stellar parameters (\teff,
\feh, $M_G$, $d$ and \ebr) of the individual stars. The algorithm is applied to
the multi-band photometric measurements of a sample of stars selected from the
SMSS survey, which have stellar parameters derived from the spectroscopic
surveys. The stellar parameters derived from multi-band photometries by our
algorithm are in good agreement with those from the spectroscopic surveys. The
typical differences between our results and the literature values are 170\,K
for \teff, 0.23\,dex for \feh, 0.13\,mag for $M_G$ and 0.05\,mag for \ebr. The
algorithm proved to be robust and effective and will be applied to the data of
future large-scale photometric surveys such as the Mephisto and CSST surveys.
