  The distribution of nuclear ground-state spin in the two-body random ensemble
(TBRE) is studied by using a general classification neural network (NN) model
with the two-body interaction matrix elements as input features and
corresponding ground-state spins as labels or output predictions. It seems that
quantum many-body system problem exceeds the capability of our optimized neural
networks when it comes to accurately predicting the ground-state spin of each
sample within the TBRE. However, our neural network model effectively captures
the statistical properties of the ground-state spin. This may be attributed to
the fact that the neural network (NN) model has learned the empirical
regularity of the ground-state spin distribution in TBRE, as discovered by
human physicists.
