  Since Gaia DR2 was released, many velocity structures in the disk have been
revealed such as large scale ridge-like patterns in the phase space. Both
kinematic information and stellar elemental abundances are needed to reveal
their evolution history. We have used labels from the APOGEE survey to predict
elemental abundances for a huge amount of low resolution spectra from the
LAMOST survey. Deep learning with artificial neural networks can automatically
draw on physically sensible features in the spectrum for their predictions.
Abundances of 12 individual elements: [C/Fe], [N/Fe], [O/Fe], [Mg/Fe], [Al/Fe],
[Si/Fe], [S/Fe], [Cl/Fe], [Ca/Fe], [Ti/Fe], [Mn/Fe] and [Ni/Fe] along with
basic stellar labels $T_{\textrm{eff}}$, log $g$, metallicity ([M/H] and
[Fe/H]) and [$\alpha$/M] for 1 063 386 stars have been estimated. Then those
stars were cross matched with Gaia DR2 data to obtain kinematic parameters. We
presented distributions of chemical abundances in the $V_{\phi}$ versus
\textit{R} coordinate. Our results extend the chemical characterization of the
ridges in the (R, $V_{\phi}$) plane to about $R = $13 kpc toward the anticenter
direction. In addition, radial elemental abundance gradients for disk stars
with abs(\textit{z})$ < 0.5$ kpc are investigated and we fitted a line for
median abundance values of bins of stars with galactocentric distance between
$R > 7.84$ kpc and $R < 15.84$ kpc. The radial metallicity gradients for disk
stars are respectively -0.0475 $\pm $0.0015 for $R < 13.09$ kpc and -0.0173
$\pm $0.0028 for $R < 13.09$ kpc. Gradients for other elemental abundances are
also obtained, for example, [$\alpha$/M] gradients for disk stars is 0.0030
$\pm $0.0002.
