Acceleration processes that occur in astrophysical plasmas produce cosmic rays that are observed on Earth. To study particle acceleration, fully-kinetic particle-in-cell (PIC) simulations are often used as they can unveil the microphysics of energization processes. Tracing of individual particles in PIC simulations is particularly useful in this regard. However, by-eye inspection of particle trajectories includes a high level of bias and uncertainty in pinpointing specific acceleration mechanisms that affect particles. Here we present a new approach that uses neural networks to aid individual particle data analysis. We demonstrate this approach on the test data that consists of 252,000 electrons which have been traced in a PIC simulation of a non-relativistic high Mach number perpendicular shock, in which we observe the two-stream electrostatic Buneman instability to pre-accelerate a portion of electrons to nonthermal energies. We perform classification, regression and anomaly detection by using a Convolutional Neural Network. We show that regardless of how noisy and imbalanced the datasets are, the regression and classification are able to predict the final energies of particles with high accuracy, whereas anomaly detection is able to discern between energetic and non-energetic particles. The methodology proposed may considerably simplify particle classification in large-scale PIC and also hybrid kinetic simulations.