  In the forthcoming era of big astronomical data, it is a burden to find out
target sources from ground-based and space-based telescopes. Although Machine
Learning (ML) methods have been extensively utilized to address this issue, the
incorporation of in-depth data analysis can significantly enhance the
efficiency of identifying target sources when dealing with massive volumes of
astronomical data. In this work, we focused on the task of finding AGN
candidates and identifying BL Lac/FSRQ candidates from the 4FGL DR3 uncertain
sources. We studied the correlations among the attributes of the 4FGL DR3
catalogue and proposed a novel method, named FDIDWT, to transform the original
data. The transformed dataset is characterized as low-dimensional and
feature-highlighted, with the estimation of correlation features by Fractal
Dimension (FD) theory and the multi-resolution analysis by Inverse Discrete
Wavelet Transform (IDWT). Combining the FDIDWT method with an improved
lightweight MatchboxConv1D model, we accomplished two missions: (1) to
distinguish the Active Galactic Nuclei (AGNs) from others (Non-AGNs) in the
4FGL DR3 uncertain sources with an accuracy of 96.65%, namely, Mission A; (2)
to classify blazar candidates of uncertain type (BCUs) into BL Lacertae objects
(BL Lacs) or Flat Spectrum Radio Quasars (FSRQs) with an accuracy of 92.03%,
namely, Mission B. There are 1354 AGN candidates in Mission A, 482 BL Lacs
candidates and 128 FSRQ candidates in Mission B were found. The results show a
high consistency of greater than 98% with the results in previous works. In
addition, our method has the advantage of finding less variable and relatively
faint sources than ordinary methods.
