  Close white dwarf binaries (CWDBs) are considered to be progenitors of
several exotic astronomical phenomena (e.g., type Ia supernovae, cataclysmic
variables). These violent events are broadly used in studies of general
relativity and cosmology. However, obtaining precise stellar parameter
measurements for both components of CWDBs is a challenging task given their low
luminosities, swift time variation, and complex orbits. High-resolution spectra
(R$> 20 000$) are preferred but expensive, resulting in a sample size that is
insufficient for robust population study. To release the full potential of the
less expensive low-resolution spectroscopic surveys, and thus greatly expand
the CWDB sample size, it is necessary to develop a robust pipeline for spectra
decomposition and analysis. We used an artificial neural network (ANN) to build
spectrum generators for DA/DB white dwarfs and main-sequence stars. The
best-fit stellar parameters were obtained by finding the least $\chi^2$
solution to these feature lines and the continuum simultaneously. We
demonstrate the reliability of our code with two well-studied CWDBs, WD
1534+503 and PG 1224+309. We also estimate the stellar parameters of 14 newly
identified CWDB candidates, most of which are fitted with double component
models for the first time. Our estimates agree with previous results for the
common stars and follow the statistical distribution in the literature. The
application of our code to a large volume of white dwarf binary candidates will
offer important statistic samples to stellar evolution studies and future
gravitational wave monitoring.
