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In this paper we propose to use convolutional neural networks (CNNs) toimprove the precision measurement of the Higgs boson-gluon effective couplingat 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 gluonpair and the $Z$ boson decaying to a lepton pair at the center-of-mass energy250 GeV and integrated luminosity 5 ab$^{-1}$. By using CNNs, the uncertaintyof 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 theHERWIG data in the Monte Carlo simulation. Moreover, the performance of CNNsusing different final state constituents shows that the energy distributions ofthe leading and subleading jets constituents play a major role in theidentification and the optimal uncertainty of effective coupling using CNNs isreduced by about $35\%$ compared to that using conventional method.

Improving the measurement of the Higgs boson-gluon coupling using convolutional neural networks at $e^+e^-$ colliders