  Accurate measurements of statistical properties, such as the star formation
rate and the lifetime of young stellar objects (YSOs) in different stages, is
essential for constraining star formation theories. However, it is a difficult
task to separate galaxies and YSOs based on spectral energy distributions
(SEDs) alone, because they contain both thermal emission from stars and dust
around them and no reliable theories can be applied to distinguish them. Here
we compare different machine learning algorithms and develop the Spectrum
Classifier of Astronomical Objects (SCAO), based on Fully Connected Neural
Network (FCN), to classify regular stars, galaxies, and YSOs. Superior to
previous classifiers, SCAO is solely trained by high quality data labeled in
Molecular Cores to Planet-forming Disks (c2d) catalog without a priori
theoretical knowledge, and provides excellent results with high precision
(>96%) and recall (>98%) for YSOs when only eight bands are included. We
systematically investigate the effects of observation errors and distance
effects, and show that high accuracy performance is still maintained even when
using fluxes of only three bands (IRAC 3, IRAC 4, and MIPS 1) in the long
wavelengths regime, because the silicate absorption feature is automatically
detected by SCAO. Finally, we apply SCAO to Spitzer Enhanced Imaging Products
(SEIP), the most complete catalog of Spitzer observations, and found 129219 YSO
candidates. The website from SCAO is available at http://scao.astr.nthu.edu.tw.
