  Hot subdwarf stars are core He burning stars located at the blue end of the
horizontal branch, also known as the extreme horizontal branch. The properties
of hot subdwarf stars are important for our understanding of the stellar
astrophysics, globular clusters and galaxies. The spectra of hot subdwarf stars
can provide us with the detailed information of the stellar atmospheric
parameters (such as effective temperature, gravity, and helium abundances),
which is important to clarify the astrophysical and statistical properties of
hot subdwarf stars. These properties can provide important constraint on the
theoretical models of hot subdwarf stars. Searching for hot subdwarf stars from
the spectra data obtained by the Large Sky Area Multi-Object Fiber
Spectroscopic Telescope (LAMOST) can significantly enlarge the sample size of
hot subdwarf stars, and help us better study the nature of hot subdwarf stars.
In this paper we study a new method of searching for hot subdwarf stars from
LAMOST spectra using convolutional neural networks and support vector machine
(CNN+SVM). The experiment on the spectra from LAMOST DR4 shows that CNN+SVM can
classify the hot subdwarf stars accurately: the accuracy is 88.98$\%$ and the
recall is 94.38 $\%$. Our research provides a new machine learning tool for
searching for hot subdwarf stars in large spectroscopic surveys.
