  Most of exotic resonances observed in the past decade appear as peak
structure near some threshold. These near-threshold phenomena can be
interpreted as genuine resonant states or enhanced threshold cusps. Apparently,
there is no straightforward way of distinguishing the two structures. In this
work, we employ the strength of deep feed-forward neural network in classifying
objects with almost similar features. We construct a neural network model with
scattering amplitude as input and nature of pole causing the enhancement as
output. The training data is generated by an S-matrix satisfying the unitarity
and analyticity requirements. Using the separable potential model, we generate
a validation data set to measure the network's predictive power. We find that
our trained neural network model gives high accuracy when the cut-off parameter
of the validation data is within $400$-$800\mbox{ MeV}$. As a final test, we
use the Nijmegen partial wave and potential models for nucleon-nucleon
scattering and show that the network gives the correct nature of pole.
