  Hybrid density functional calculation is indispensable to accurate
description of electronic structure, whereas the formidable computational cost
restricts its broad application. Here we develop a deep equivariant neural
network method (named DeepH-hybrid) to learn the hybrid-functional Hamiltonian
from self-consistent field calculations of small structures, and apply the
trained neural networks for efficient electronic-structure calculation by
passing the self-consistent iterations. The method is systematically checked to
show high efficiency and accuracy, making the study of large-scale materials
with hybrid-functional accuracy feasible. As an important application, the
DeepH-hybrid method is applied to study large-supercell Moir\'{e} twisted
materials, offering the first case study on how the inclusion of exact exchange
affects flat bands in the magic-angle twisted bilayer graphene.
