  We present data preprocessing based on an artificial neural network to
estimate the parameters of the X-ray emission spectra of a single-temperature
thermal plasma. The method finds appropriate parameters close to the global
optimum. The neural network is designed to learn the parameters of the thermal
plasma (temperature, abundance, normalisation, and redshift) of the input
spectra. After training using 9000 simulated X-ray spectra, the network has
grown to predict all the unknown parameters with uncertainties of about a few
percent. The performance dependence on the network structure has been studied.
We applied the neural network to an actual high-resolution spectrum obtained
with {\it Hitomi}. The predicted plasma parameters agreed with the known
best-fit parameters of the Perseus cluster within $\lesssim10$\% uncertainties.
The result shows a possibility that neural networks trained by simulated data
can be useful to extract a feature built in the data, which would reduce
human-intensive preprocessing costs before detailed spectral analysis, and help
us make the best use of large quantities of spectral data coming in the next
decades.
