  We suggest a method that evaluates the horizontal velocity in the solar
photosphere with easily observable values using a combination of neural network
and radiative magnetohydrodynamics simulations. All three-component velocities
of thermal convection on the solar surface have important roles in generating
waves in the upper atmosphere. However, the velocity perpendicular to the line
of sight (LoS) is difficult to observe. To deal with this problem, the local
correlation tracking (LCT) method, which employs the difference between two
images, has been widely used, but LCT has several disadvantages. We develop a
method that evaluates the horizontal velocity from a snapshot of the intensity
and the LoS velocity with a neural network. We use data from numerical
simulations for training the neural network. While two consecutive intensity
images are required for LCT, our network needs just one intensity image at only
a specific moment for input. From these input array, our network outputs a
same-size array of two-component velocity field. With only the intensity data,
the network achieves a high correlation coefficient between the simulated and
evaluated velocities of 0.83. In addition, the network performance can be
improved when we add LoS velocity for input, enabling achieving a correlation
coefficient of 0.90. Our method is also applied to observed data.
