  In the paper, it is discussed by using Monte-Carlo simulation that the
Bayesian Neural Network(BNN) is applied to determine neutrino incoming
direction in reactor neutrino experiments and supernova explosion location by
scintillator detectors. As a result, compared to the method in
Ref.\cite{key-1}, the uncertainty on the measurement of the neutrino direction
using BNN is significantly improved. The uncertainty on the measurement of the
reactor neutrino direction is about 1.0$^\circ$ at the 68.3% C.L., and the one
in the case of supernova neutrino is about 0.6$^\circ$ at the 68.3% C.L..
Compared to the method in Ref.\cite{key-1}, the uncertainty attainable by using
BNN reduces by a factor of about 20. And compared to the Super-Kamiokande
experiment(SK), it reduces by a factor of about 8.
