  Employing a new machine learning method, named hierarchical extreme learning
machine (HELM) algorithm, we identified 56 hot subdwarf stars in the first data
release (DR1) of the Large Sky Area Multi-Object Fibre Spectroscopic Telescope
(LAMOST) survey. The atmospheric parameters of the stars are obtained by
fitting the profiles of hydrogen (H) Balmer lines and helium (He) lines with
synthetic spectra calculated from non-Local Thermodynamic Equilibrium (NLTE)
model atmospheres. Five He-rich hot subdwarf stars were found in our sample
with their log(nHe/nH) > -1 , while 51 stars are He-poor sdB, sdO and sdOB
stars. We also confirmed the two He sequences of hot subdwarf stars found by
Edelmann et al. (2003) in Teff - log(nHe/nH) diagram. The HELM algorithm works
directly on the observed spectroscopy and is able to filter out spectral
properties without supplementary photometric data. The results presented in
this study demonstrate that the HELM algorithm is a reliable method to search
for hot subdwarf stars after a suitable training is performed, and it is also
suitable to search for other objects which have obvious features in their
spectra or images.
