  The TAIGA experimental complex is a hybrid observatory for high-energy
gamma-ray astronomy in the range from 10 TeV to several EeV. The complex
consists of such installations as TAIGA- IACT, TAIGA-HiSCORE and a number of
others. The TAIGA-HiSCORE facility is a set of wide-angle synchronized stations
that detect Cherenkov radiation scattered over a large area. TAIGA-HiSCORE data
provides an opportunity to reconstruct shower characteristics, such as shower
energy, direction of arrival, and axis coordinates. The main idea of the work
is to apply convolutional neural networks to analyze HiSCORE events,
considering them as images. The distribution of registration times and
amplitudes of events recorded by HiSCORE stations is used as input data. The
paper presents the results of using convolutional neural networks to determine
the characteristics of air showers. It is shown that even a simple model of
convolutional neural network provides the accuracy of recovering EAS parameters
comparable to the traditional method. Preliminary results of air shower
parameters reconstruction obtained in a real experiment and their comparison
with the results of traditional analysis are presented.
