  NOvA is a long-baseline neutrino oscillation experiment. It is optimized to
measure $\nu_e$ appearance and $\nu_{\mu}$ disappearance at the Far Detector in
the $\nu_{\mu}$ beam produced by the NuMI facility at Fermilab. NOvA uses a
convolutional neural network (CNN) to identify neutrino events in two
functionally identical liquid scintillator detectors. A different network,
called prong-CNN, has been used to classify reconstructed particles in each
event as either lepton or hadron. Within each event, hits are clustered into
prongs to reconstruct final-state particles and these prongs form the input to
this prong-CNN classifier. Classified particle energies are then used as input
to an electron neutrino energy estimator. Improving the resolution and
systematic robustness of NOvA's energy estimator will improve the sensitivity
of the oscillation parameters measurement. This paper describes the methods to
identify particles with prong-CNN and the following approach to estimate
$\nu_e$ energy for signal events.
