  The application of Bayesian Neural Networks(BNN) to discriminate neutrino
events from backgrounds in reactor neutrino experiments has been described in
Ref.\cite{key-1}. In the paper, BNN are also used to identify neutrino events
in reactor neutrino experiments, but the numbers of photoelectrons received by
PMTs are used as inputs to BNN in the paper, not the reconstructed energy and
position of events. The samples of neutrino events and three major backgrounds
from the Monte-Carlo simulation of a toy detector are generated in the signal
region. Compared to the BNN method in Ref.\cite{key-1}, more $^{8}$He/$^{9}$Li
background and uncorrelated background in the signal region can be rejected by
the BNN method in the paper, but more fast neutron background events in the
signal region are unidentified using the BNN method in the paper. The
uncorrelated background to signal ratio and the $^{8}$He/$^{9}$Li background to
signal ratio are significantly improved using the BNN method in the paper in
comparison with the BNN method in Ref.\cite{key-1}. But the fast neutron
background to signal ratio in the signal region is a bit larger than the one in
Ref.\cite{key-1}.
