  Morphological classification conveys abundant information on the formation,
evolution, and environment of galaxies. In this work, we refine the two-step
galaxy morphological classification framework ({\tt\string USmorph}), which
employs a combination of unsupervised machine learning (UML) and supervised
machine learning (SML) techniques, along with a self-consistent and robust data
preprocessing step. The updated method is applied to the galaxies with $I_{\rm
mag}<25$ at $0.2<z<1.2$ in the COSMOS field. Based on their HST/ACS I-band
images, we classify them into five distinct morphological types: spherical
(SPH, 15,200), early-type disk (ETD, 17,369), late-type disk (LTD, 21,143),
irregular disk (IRR, 28,965), and unclassified (UNC, 17,129). In addition, we
have conducted both parametric and nonparametric morphological measurements.
For galaxies with stellar masses exceeding $10^{9}M_{\sun}$, a gradual increase
in effective radius from SPHs to IRRs is observed, accompanied by a decrease in
the S\'{e}rsic index. Nonparametric morphologies reveal distinct distributions
of galaxies across the $Gini-M_{20}$ and $C-A$ parameter spaces for different
categories. Moreover, different categories exhibit significant dissimilarity in
their $G_2$ and $\Psi$ distributions. We find morphology to be strongly
correlated with redshift and stellar mass. The consistency of these
classification results with expected correlations among multiple parameters
underscores the validity and reliability of our classification method,
rendering it a valuable tool for future studies.
