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

SPar: estimating stellar parameters from multi-band photometries with empirical stellar libraries