  The fundamental stellar atmospheric parameters T_eff and log g and 13
chemical abundances are derived for medium-resolution spectroscopy from LAMOST
Medium-Resolution Survey (MRS) data sets with a deep-learning method. The
neural networks we designed, named as SPCANet, precisely map LAMOST MRS spectra
to stellar parameters and chemical abundances. The stellar labels derived by
SPCANet are with precisions of 119 K for T_eff and 0.17 dex for log g. The
abundance precision of 11 elements including [C/H], [N/H], [O/H], [Mg/H],
[Al/H], [Si/H], [S/H], [Ca/H], [Ti/H], [Cr/H], [Fe/H], and [Ni/H] are 0.06~0.12
dex, while of [Cu/H] is 0.19 dex. These precisions can be reached even for
spectra with signal-to-noise as low as 10. The results of SPCANet are
consistent with those from other surveys such as APOGEE, GALAH and RAVE, and
are also validated with the previous literature values including clusters and
field stars. The catalog of the estimated parameters is available at
\url{http://paperdata.china-vo.org/LAMOST/MRS_parameters_elements.csv}.

Solar and Stellar Astrophysics;Astrophysics of Galaxies