  In order to obtain morphological information of unlabeled galaxies, we
present an unsupervised machine-learning (UML) method for morphological
classification of galaxies, which can be summarized as two aspects: (1) the
methodology of convolutional autoencoder (CAE) is used to reduce the dimensions
and extract features from the imaging data; (2) the bagging-based
multiclustering model is proposed to obtain the classifications with high
confidence at the cost of rejecting the disputed sources that are
inconsistently voted. We apply this method on the sample of galaxies with
$H<24.5$ in CANDELS. Galaxies are clustered into 100 groups, each contains
galaxies with analogous characteristics. To explore the robustness of the
morphological classifications, we merge 100 groups into five categories by
visual verification, including spheroid, early-type disk, late-type disk,
irregular, and unclassifiable. After eliminating the unclassifiable category
and the sources with inconsistent voting, the purity of the remaining four
subclasses are significantly improved. Massive galaxies ($M_*>10^{10}M_\odot$)
are selected to investigate the connection with other physical properties. The
classification scheme separates galaxies well in the U-V and V-J color space
and Gini-$M_{20}$ space. The gradual tendency of S\'{e}rsic indexes and
effective radii is shown from the spheroid subclass to the irregular subclass.
It suggests that the combination of CAE and multi-clustering strategy is an
effective method to cluster galaxies with similar features and can yield
high-quality morphological classifications. Our study demonstrates the
feasibility of UML in morphological analysis that would develop and serve the
future observations made with China Space Station telescope.
