  This work proposes a Residual Recurrent Neural Network (RRNet) for
synthetically extracting spectral information, and estimating stellar
atmospheric parameters together with 15 chemical element abundances for
medium-resolution spectra from Large Sky Area Multi-Object Fiber Spectroscopic
Telescope (LAMOST). The RRNet consists of two fundamental modules: a residual
module and a recurrent module. The residual module extracts spectral features
based on the longitudinally driving power from parameters, while the recurrent
module recovers spectral information and restrains the negative influences from
noises based on Cross-band Belief Enhancement. RRNet is trained by the spectra
from common stars between LAMOST DR7 and APOGEE-Payne catalog. The 17 stellar
parameters and their uncertainties for 2.37 million medium-resolution spectra
from LAMOST DR7 are predicted. For spectra with S/N >= 10, the precision of
estimations Teff and log g are 88 K and 0.13 dex respectively, elements C, Mg,
Al, Si, Ca, Fe, Ni are 0.05 dex to 0.08 dex, and N, O, S, K, Ti, Cr, Mn are
0.09 dex to 0.14 dex, while that of Cu is 0.19 dex. Compared with StarNet and
SPCANet, RRNet shows higher accuracy and robustness. In comparison to Apache
Point Observatory Galactic Evolution Experiment and Galactic Archaeology with
HERMES surveys, RRNet manifests good consistency within a reasonable range of
bias. Finally, this work releases a catalog for 2.37 million medium-resolution
spectra from the LAMOST DR7, the source code, the trained model and the
experimental data respectively for astronomical science exploration and data
processing algorithm research reference.
