  A tagging algorithm to identify jets that are significantly displaced from
the proton-proton (pp) collision region in the CMS detector at the LHC is
presented. Displaced jets can arise from the decays of long-lived particles
(LLPs), which are predicted by several theoretical extensions of the standard
model. The tagger is a multiclass classifier based on a deep neural network,
which is parameterised according to the proper decay length $\mathrm{c}\tau_0$
of the LLP. A novel scheme is defined to reliably label jets from LLP decays
for supervised learning. Samples of pp collision data, recorded by the CMS
detector at a centre-of-mass energy of 13 TeV, and simulated events are used to
train the neural network. Domain adaptation by backward propagation is
performed to improve the simulation modelling of the jet class probability
distributions observed in pp collision data. The potential performance of the
tagger is demonstrated with a search for long-lived gluinos, a manifestation of
split supersymmetric models. The tagger provides a rejection factor of 10 000
for jets from standard model processes, while maintaining an LLP jet tagging
efficiency of 30-80% for gluinos with 1 mm $\leq$ $c\tau_0$ $\leq$ 10 m. The
expected coverage of the parameter space for split supersymmetry is presented.
