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The fundamental stellar atmospheric parameters T_eff and log g and 13chemical abundances are derived for medium-resolution spectroscopy from LAMOSTMedium-Resolution Survey (MRS) data sets with a deep-learning method. Theneural networks we designed, named as SPCANet, precisely map LAMOST MRS spectrato stellar parameters and chemical abundances. The stellar labels derived bySPCANet are with precisions of 119 K for T_eff and 0.17 dex for log g. Theabundance 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 0.06~0.12dex, while of [Cu/H] is 0.19 dex. These precisions can be reached even forspectra with signal-to-noise as low as 10. The results of SPCANet areconsistent with those from other surveys such as APOGEE, GALAH and RAVE, andare also validated with the previous literature values including clusters andfield stars. The catalog of the estimated parameters is available at\url{http://paperdata.china-vo.org/LAMOST/MRS_parameters_elements.csv}.

SPCANet: Stellar Parameters and Chemical Abundances Network for LAMOST-II Medium Resolution Survey