  TAIGA is a hybrid observatory for gamma-ray astronomy at high energies in
range from 10 TeV to several EeV. It consists of instruments such as
TAIGA-IACT, TAIGA-HiSCORE, and others. TAIGA-HiSCORE, in particular, is an
array of wide-angle timing Cherenkov light stations. TAIGA-HiSCORE data enable
to reconstruct air shower characteristics, such as air shower energy, arrival
direction, and axis coordinates. In this report, we propose to consider the use
of convolution neural networks in task of air shower characteristics
determination. We use Convolutional Neural Networks (CNN) to analyze HiSCORE
events, treating them like images. For this, the times and amplitudes of events
recorded at HiSCORE stations are used. The work discusses a simple
convolutional neural network and its training. In addition, we present some
preliminary results on the determination of the parameters of air showers such
as the direction and position of the shower axis and the energy of the primary
particle and compare them with the results obtained by the traditional method.
