  This article investigates the problem of estimating stellar atmospheric
parameters from spectra. Feature extraction is a key procedure in estimating
stellar parameters automatically. We propose a scheme for spectral feature
extraction and atmospheric parameter estimation using the following three
procedures: firstly, learn a set of basic structure elements (BSE) from stellar
spectra using an autoencoder; secondly, extract representative features from
stellar spectra based on the learned BSEs through some procedures of
convolution and pooling; thirdly, estimate stellar parameters ($T_{eff}$,
log$~g$, [Fe/H]) using a back-propagation (BP) network. The proposed scheme has
been evaluated on both real spectra from Sloan Digital Sky Survey (SDSS)/Sloan
Extension for Galactic Understanding and Exploration (SEGUE) and synthetic
spectra calculated from Kurucz's new opacity distribution function (NEWODF)
models. The best mean absolute errors (MAEs) are 0.0060 dex for log$~T_{eff}$,
0.1978 dex for log$~g$ and 0.1770 dex for [Fe/H] for the real spectra and
0.0004 dex for log$~T_{eff}$, 0.0145 dex for log$~g$ and 0.0070 dex for [Fe/H]
for the synthetic spectra.
