  Supersymmetry with hadronic R-parity violation in which the lightest
neutralino decays into three quarks is still weakly constrained. This work aims
to further improve the current search for this scenario by the boosted decision
tree method with additional information from jet substructure. In particular,
we find a deep neural network turns out to perform well in characterizing the
neutralino jet substructure. We first construct a Convolutional Neutral Network
(CNN) which is capable of tagging the neutralino jet in any signal process by
using the idea of jet image. When applied to pure jet samples, such a CNN
outperforms the N-subjettiness variable by a factor of a few in tagging
efficiency. Moreover, we find the method, which combines the CNN output and jet
invariant mass, can perform better and is applicable to a wider range of
neutralino mass than the CNN alone. Finally, the ATLAS search for the signal of
gluino pair production with subsequent decay $\tilde{g} \to q q
\tilde{\chi}^0_1 (\to q q q)$ is recasted as an application. In contrast to the
pure sample, the heavy contamination among jets in this complex final state
renders the discriminating powers of the CNN and N-subjettiness similar. By
analyzing the jets substructure in events which pass the ATLAS cuts with our
CNN method, the exclusion limit on gluino mass can be pushed up by $\sim200$
GeV for neutralino mass $\sim 100$ GeV.
