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.
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