Accurate measurements of statistical properties, such as the star formation rate and the lifetime of young stellar objects (YSOs) in different stages, are 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, a=IRAC 4, and MIPS 1) in the long wavelengths regime, because the silicate absorption feature is automatically detected by SCAO. Finally, we applied 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.

准确测量统计特性，例如恒星形成率和不同阶段的年轻恒星物体（YSO）的寿命，对于限制恒星形成理论至关重要。但是，仅根据光谱能量分布（SED）分离星系和YSO是一项艰巨的任务，因为它们既包含来自恒星的热辐射，又包含围绕它们的尘埃，并且无法应用可靠的理论来区分它们。在这里，我们比较不同的机器学习算法，并基于全连接神经网络（FCN）开发天文物体的光谱分类器（SCAO），以对常规恒星，星系和YSO进行分类。优于先前的分类器，SCAO仅接受由分子核标记为行星形成磁盘（c2d）目录中标记的高质量数据进行训练，而无需先验的理论知识，$>$96％）和召回率（$>$如果只包含八个频段，则为YSO提供98％的费用）。我们系统地研究了观测误差和距离效应的影响，并表明即使在长波长范围内仅使用三个波段（IRAC 3，a = IRAC 4和MIPS 1）的通量时，仍能保持高精度性能，因为硅酸盐吸收特征由SCAO自动检测。最后，我们将SCAO应用于Spitzer观测最完整的目录Spitzer增强成像产品（SEIP），并找到了129219个YSO候选对象。SCAO的网站可在http://scao.astr.nthu.edu.tw获得。