  Studying the cosmological sources at their cosmological rest-frames is
crucial to track the cosmic history and properties of compact objects. In view
of the increasing data volume of existing and upcoming telescopes/detectors, we
here construct a 1--dimensional convolutional neural network (CNN) with a
residual neural network (ResNet) structure to estimate the redshift of quasars
in Sloan Digital Sky Survey IV (SDSS-IV) catalog from DR16 quasar-only (DR16Q)
of eBOSS on a broad range of signal-to-noise ratios, named \code{FNet}. Owing
to its $24$ convolutional layers and the ResNet structure with different kernel
sizes of $500$, $200$ and $15$, FNet is able to discover the "\textit{local}"
and "\textit{global}" patterns in the whole sample of spectra by a
self-learning procedure. It reaches the accuracy of 97.0$\%$ for the velocity
difference for redshift, $|\Delta\nu|< 6000~ \rm km/s$ and 98.0$\%$ for
$|\Delta\nu|< 12000~ \rm km/s$. While \code{QuasarNET}, which is a standard CNN
adopted in the SDSS routine and is constructed by 4 convolutional layers (no
ResNet structure), with kernel sizes of $10$, to measure the redshift via
identifying seven emission lines (\textit{local} patterns), fails in estimating
redshift of $\sim 1.3\%$ of visually inspected quasars in DR16Q catalog, and it
gives 97.8$\%$ for $|\Delta\nu|< 6000~ \rm km/s$ and 97.9$\%$ for $|\Delta\nu|<
12000~ \rm km/s$. Hence, FNet provides similar accuracy to \code{QuasarNET},
but it is applicable for a wider range of SDSS spectra, especially for those
missing the clear emission lines exploited by \code{QuasarNET}. These
properties of \code{FNet}, together with the fast predictive power of machine
learning, allow \code{FNet} to be a more accurate alternative for the pipeline
redshift estimator and can make it practical in the upcoming catalogs to reduce
the number of spectra to visually inspect.
