  A small fraction of giants possess photospheric lithium(Li) abundance higher
than the value predicted by the standard stellar evolution models, and the
detailed mechanisms of Li enhancement are complicated and lack a definite
conclusion. In order to better understand the Li enhancement behaviors, a large
and homogeneous Li-rich giants sample is needed. In this study, we designed a
modified convolutional neural network model called Coord-DenseNet to determine
the A(Li) of Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST)
low-resolution survey (LRS) giant spectra. The precision is good on the test
set: MAE=0.15 dex, and {\sigma}=0.21 dex. We used this model to predict the Li
abundance of more than 900,000 LAMOST DR8 LRS giant spectra and identified
7,768 Li-rich giants with Li abundances ranging from 2.0 to 5.4 dex, accounting
for about 1.02% of all giants. We compared the Li abundance estimated by our
work with those derived from high-resolution spectra. We found that the
consistency was good if the overall deviation of 0.27 dex between them was not
considered. The analysis shows that the difference is mainly due to the high
A(Li) from the medium-resolution spectra in the training set. This sample of
Li-rich giants dramatically expands the existing sample size of Li-rich giants
and provides us with more samples to further study the formation and evolution
of Li-rich giants.
