Topology　optimization　is　a　common　approach　for　material　distribution　in　continuous　structure　due　to　its　rigorous　mathematical　theory.　However,　with　the　increase　of　material　types　in　design　domain,　the　computational　efficiency　of　traditional　topology　optimization　for　multiple　materials　problem　is　greatly　decreased.　In　this　paper,　a　novel　deep　learning-based　topology　optimization　method　is　proposed　to　achieve　multi-material　structural　design　for　improving　computational　efficiency.　A　large　number　of　multi-material　topological　configurations　are　simulated　by　solid　isotropic　material　with　penalization　(SIMP),　to　construct　multi-material　topology　optimization　dataset.　Subsequently,　ResUNet　involved　generative　adversarial　network　(ResUNet-GAN)　is　developed　for　high-dimensional　mapping　from　design　parameters　to　the　corresponding　multi-material　topological　configuration.　Finally,　the　ResUNet-GAN,　trained　by　the　multi-material　dataset,　is　utilized　to　design　multi-material　topological　configuration.　Numerical　simulations　verify　that　the　well-trained　ResUNet-GAN　is　successfully　applied　to　three　types　of　cases:　the　cantilever　beam　with　double　materials,　the　cantilever　beam　with　triple　materials,　and　the　half-MBB　with　triple　materials.　The　deep　learning-based　topology　optimization　approach　is　superior　to　the　conventional　methods　in　terms　of　higher　computational　efficiency,　performing　the　potential　of　such　a　data-driven　method　to　accelerate　the　calculation　of　structural　optimization　design.