  Significant progress in the classification of Fermi unassociated sources ,
has led to an increasing number of blazars are being found. The optical
spectrum is effectively used to classify the blazars into two groups such as BL
Lacs and flat spectrum radio quasars (FSRQs). However, the accurate
classification of the blazars without optical spectrum information, i.e.,
blazars of uncertain type (BCUs), remains a significant challenge. In this
paper, we present a principal component analysis (PCA) and machine learning
hybrid blazars classification method. The method, based on the data from Fermi
LAT 3FGL Catalog, first used the PCA to extract the primary features of the
BCUs and then used a machine learning algorithm to further classify the BCUs.
Experimental results indicate that the that the use of PCA algorithms
significantly improved the classification. More importantly, comparison with
the Fermi LAT 4FGL Catalog, which contains the spectral classification of those
BCUs in the Fermi-LAT 3FGL Catalog, reveals that the proposed classification
method in the study exhibits higher accuracy than currently established
methods; specifically, 151 out of 171 BL Lacs and 19 out of 24 FSRQs are
correctly classified.
