  Experiments to search for neutrinoless double-beta (0{\nu}\b{eta}\b{eta})
decay of 76Ge using a high-purity germanium (HPGe) detector rely heavily on
background suppression technologies to enhance their sensitivities. In this
work, we proposed a pulse-shape analysis method based on a neural network (NN)
and a light gradient boosting machine (lightGBM; LGB) to discriminate
single-electron (background) and double-electrons (0{\nu}\b{eta}\b{eta} signal)
events in a multi-electrode HPGe detector. In this paper, we describe a
multi-electrode HPGe detector system, a data-processing system, and pulse-shape
simulation procedures. We built a fully connected (FC) neural network and an
LGB model to classify the single- and double-electron events. The FC network is
trained with simulated single- and double-electron-induced pulses and tested in
an independent dataset generated by the pulse-shape simulation. The
discrimination efficiency of the FC neural network in the test set for the
0{\nu}\b{eta}\b{eta} double-electron events signal was 77.4%, the precision was
57.7%, and the training time was 430 min. The discrimination efficiency of LGB
model was 73.1%, the precision was 64.0%, and the training time was 1.5 min.
This study demonstrated that it is feasible to realize single- and
double-electron discrimination on multi-electrode HPGe detectors using an FC
neural network and LGB model. These results can be used as a reference for
future 76Ge 0{\nu}\b{eta}\b{eta} experiments.
