JUNO is a multipurpose, medium-baseline (~52 km) liquid scintillator neutrino observatory located in China. Its primary objectives are to measure the oscillation parameters theta_12, Delta m^{^2}_21, and Delta m^{^2}_31 with per mil precision and to determine the neutrino mass ordering at a 3 sigma confidence level. Achieving these goals requires an unprecedented energy resolution of 3% / sqrt(E(MeV)) with this technology. This demands a comprehensive understanding of the various effects within the detector. The Dual Calorimetry system—two distinct measurement systems observing the same event-enables not only high-precision calibration but also detection of detector effects, as demonstrated in this thesis. Deep learning, an increasingly powerful tool in physics, plays a critical role in this effort. In this thesis, I present the development, application, and analysis of Deep Learning techniques for reconstruction in the JUNO experiment.