  The accuracy of the estimated stellar atmospheric parameter decreases
evidently with the decreasing of spectral signal-to-noise ratio (SNR) and there
are a huge amount of this kind observations, especially in case of SNR$<$30.
Therefore, it is helpful to improve the parameter estimation performance for
these spectra and this work studied the ($T_\texttt{eff}, \log~g$, [Fe/H])
estimation problem for LAMOST DR8 low-resolution spectra with 20$\leq$SNR$<$30.
We proposed a data-driven method based on machine learning techniques. Firstly,
this scheme detected stellar atmospheric parameter-sensitive features from
spectra by the Least Absolute Shrinkage and Selection Operator (LASSO),
rejected ineffective data components and irrelevant data. Secondly, a
Multi-layer Perceptron (MLP) method was used to estimate stellar atmospheric
parameters from the LASSO features. Finally, the performance of the LASSO-MLP
was evaluated by computing and analyzing the consistency between its estimation
and the reference from the APOGEE (Apache Point Observatory Galactic Evolution
Experiment) high-resolution spectra. Experiments show that the Mean Absolute
Errors (MAE) of $T_\texttt{eff}, \log~g$, [Fe/H] are reduced from the LASP
(137.6 K, 0.195 dex, 0.091 dex) to LASSO-MLP (84.32 K, 0.137 dex, 0.063 dex),
which indicate evident improvements on stellar atmospheric parameter
estimation. In addition, this work estimated the stellar atmospheric parameters
for 1,162,760 low-resolution spectra with 20$\leq$SNR$<$30 from LAMOST DR8
using LASSO-MLP, and released the estimation catalog, learned model,
experimental code, trained model, training data and test data for scientific
exploration and algorithm study.
