The aim of extensive air shower (EAS) analysis is to reconstruct the physical parameters of the primary particle that initiated the shower. The TAIGA experiment is a hybrid detector system that combines several imaging atmospheric Cherenkov telescopes (IACTs) and an array of non-imaging Cherenkov detectors (TAIGA-HiSCORE) for EAS detection. Because the signals recorded by different detector types differ in physical nature, the direct merging of data is unfeasible, which complicates multimodal analysis. Currently, to analyze data from the IACTs and TAIGA-HiSCORE, a set of auxiliary parameters specific to each detector type is calculated from the recorded signals. These parameters are chosen empirically, so there is no certainty that they retain all important information and are the best suited for the respective problems. We propose to use autoencoders (AE) for the analysis of TAIGA experimental data and replace the conventionally used auxiliary parameters with the parameters of the AE latent space. The advantage of the AE latent space parameters is that they preserve essential physics from experimental data without prior assumptions. This approach also holds potential for enabling seamless integration of heterogeneous IACT and HiSCORE data through a joint latent space. To reconstruct the parameters of the primary particle of the EAS from the latent space of the AE, a separate artificial neural network is used. In this paper, the proposed approach is used to reconstruct the energy of the EAS primary particles based on Monte Carlo simulation data for TAIGA-HiSCORE. The dependence of the energy determination accuracy on the dimensionality of the latent space is analyzed, and these results are also compared with the results obtained by the conventional technique. It is shown that when using the AE latent space, the energy of the primary particle is reconstructed with satisfactory accuracy.