  A toy detector has been designed to simulate central detectors in reactor
neutrino experiments in the paper. The samples of neutrino events and three
major backgrounds from the Monte-Carlo simulation of the toy detector are
generated in the signal region. The Bayesian Neural Networks(BNN) are applied
to separate neutrino events from backgrounds in reactor neutrino experiments.
As a result, the most neutrino events and uncorrelated background events in the
signal region can be identified with BNN, and the part events each of the fast
neutron and $^{8}$He/$^{9}$Li backgrounds in the signal region can be
identified with BNN. Then, the signal to noise ratio in the signal region is
enhanced with BNN. The neutrino discrimination increases with the increase of
the neutrino rate in the training sample. However, the background
discriminations decrease with the decrease of the background rate in the
training sample.
