  In this paper we propose to use convolutional neural networks (CNNs) to
improve the precision measurement of the Higgs boson-gluon effective coupling
at lepton colliders. The CNN is employed to recognize the Higgs boson and a $Z$
boson associated production process, with the Higgs boson decaying to a gluon
pair and the $Z$ boson decaying to a lepton pair at the center-of-mass energy
250 GeV and integrated luminosity 5 ab$^{-1}$. By using CNNs, the uncertainty
of the effective coupling measurement can be decreased from $1.94\%$ to about
$1.28\%$ using the PYTHIA data and from $1.82\%$ to about $1.22\%$ using the
HERWIG data in the Monte Carlo simulation. Moreover, the performance of CNNs
using different final state constituents shows that the energy distributions of
the leading and subleading jets constituents play a major role in the
identification and the optimal uncertainty of effective coupling using CNNs is
reduced by about $35\%$ compared to that using conventional method.
