2025MNRAS.541...58L

Stellar parameters and abundances provide crucial insights into stellar and Galactic evolution studies. In this work, we developed a convolutional neural network (CNN) to estimate stellar parameters: effective temperature ($T_{\text{eff}}$), surface gravity (log g), and metallicity (both [Fe/H] and [M/H]) as well as six $\alpha$-elements (C, N, O, Mg, Si, Ca) and [$\alpha$/M]. We selected giant stars with $3500 \, \text{K} < T_{\text{eff}} < 5500 \, \text{K}$ and $0 \, \text{dex} < \log g < 3.6 \, \text{dex}$ from the LAMOST and APOGEE surveys, while requiring (S/N)$_g$ of the LAMOST low-resolution spectra $>$ 10, which leaves 1 100 858 giant stars. The spectral from LAMOST and the labels from APOGEE for 62 511 common stars were used as our training set. The corresponding test set yields scatters 50 K, 0.06 dex, and 0.13 dex for $T_{\text{eff}}$, [Fe/H] and log g, respectively. For $\alpha$ elements O, Mg, Si, and Ca, the scatters are 0.05, 0.04, 0.03, and 0.04 dex, respectively. For C and N elements, the scatters are 0.07 and 0.05 dex. For [$\alpha$/M] and [M/H], the scatters are 0.03 and 0.06 dex. The mean absolute error of most elements are between 0.02 and 0.04 dex. The predicted abundances were cross-matched with previously identified substructures PG1 and PG2, with their origins subsequently analysed. Finally, the catalogue is available at: https://nadc.china-vo.org/res/r101529/.