  We study the tagging of Higgs exotic decay signals using different types of
deep neural networks (DNNs), focusing on the $W^\pm h$ associated production
channel followed by Higgs decaying into $n$ $b$-quarks with $n=4$, 6 and 8. All
the Higgs decay products are collected into a fat-jet, to which we apply
further selection using the DNNs. Three kinds of DNNs are considered, namely
convolutional neural network (CNN), recursive neural network (RecNN) and
particle flow network (PFN). The PFN can achieve the best performance because
its structure allows enfolding more information in addition to the
four-momentums of the jet constituents, such as particle ID and tracks
parameters. Using the PFN as an example, we verify that it can serve as an
efficient tagger even though it is trained on a different event topology with
different $b$-multiplicity from the actual signal. The projected sensitivity to
the branching ratio of Higgs decaying into $n$ $b$-quarks at the HL-LHC are
10\%, 3\% and 1\%, for $n=4$, 6 and 8, respectively.
