NOvA is a long baseline neutrino oscillation experiment. It is optimized to measure νe appearance and νμ disappearance at the Far Detector in the νμ beam produced by the NuMI facility at Fermilab. NOvA uses a convolutional neural network(CVN) to identify neutrino events in two functionally identical liquid scintillator detectors. A different network, called “Prong-CVN”, has been used to classify reconstructed particles in each event as either lepton or hadron. Within each event, hits are clustered into prongs to recon- struct final state particles and these prongs form the input to this new 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 measurements. In this talk, I will present our methods to identify particles with Prong-CVN and the following approach to estimate νe energy for signal events.