Deep learning algorithms have gained importance in astroparticle physics in the last years. They have been shown to outperform traditional strategies in particle identification, tracking and energy reconstruction. The attractive feature of these techniques is their ability to model large dimensionality inputs and catch non-trivial correlations among the variables, which could be hidden or not easy to model. This contribution focuses on the application of deep neural networks to the event reconstruction of the Limadou High-Energy Particle Detector on board of the China Seismo-Electromagnetic Satellite. We describe the model adopted for the neural network and report on the performance measured on simulated and real data.