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As a typical data-driven method, deep learning becomes a natural choice foranalysing astronomical data nowadays. In this study, we built a deepconvolutional neural network to estimate basic stellar parameters$T\rm{_{eff}}$, log g, metallicity ([M/H] and [Fe/H]) and [$\alpha$/M] alongwith nine individual elemental abundances ([C/Fe], [N/Fe], [O/Fe], [Mg/Fe],[Al/Fe], [Si/Fe], [Ca/Fe], [Mn/Fe], [Ni/Fe]). The neural network is trainedusing common stars between the APOGEE survey and the LAMOST survey. We usedlow-resolution spectra from LAMOST survey as input, and measurements fromAPOGEE as labels. For stellar spectra with the signal-to-noise ratio in g bandlarger than 10 in the test set, the mean absolute error (MAE) is 29 K for$T\rm{_{eff}}$, 0.07 dex for log g, 0.03 dex for both [Fe/H] and [M/H], and0.02 dex for [$\alpha$/M]. The MAE of most elements is between 0.02 dex and0.04 dex. The trained neural network was applied to 1,210,145 giants, includingsub-giants, from LAMOST DR8 within the range of stellar parameters 3500 K <$T\rm{_{eff}}$ < 5500 K, 0.0 dex < log g < 4.0 dex, -2.5 dex < [Fe/H] < 0.5dex. The distribution of our results in the chemical spaces is highlyconsistent with APOGEE labels and stellar parameters show consistency withexternal high-resolution measurements from GALAH. The results in this studyallow us to further studies based on LAMOST data and deepen our understandingof the accretion and evolution history of the Milky Way. The electronic versionof the value added catalog is available athttp://www.lamost.org/dr8/v1.1/doc/vac.

The stellar parameters and elemental abundances from low-resolution spectra I: 1.2 million giants from LAMOST DR8