This　paper　presents　a　deep　learning-based　topology　optimization　method　for　the　joint　design　of　material　layout　and　fiber　orientation　in　continuous　fiber-reinforced　composite　structure　(CFRCS).　The　proposed　method　mainly　includes　three　steps:　(1)　a　ResUNet-involved　generative　and　adversarial　network　(ResUNet-GAN)　is　developed　to　establish　the　end-to-end　mapping　from　structural　design　parameters　to　fiber-reinforced　composite　optimized　structure,　and　a　fiber　orientation　chromatogram　is　presented　to　represent　continuous　fiber　angles;　(2)　to　avoid　the　local　optimum　problem,　the　independent　continuous　mapping　method　(ICM　method)　considering　the　improved　principal　stress　orientation　interpolated　continuous　fiber　angle　optimization　(PSO-CFAO)　strategy　is　utilized　to　construct　CFRCS　topology　optimization　dataset;　(3)　the　well-trained　ResUNet-GAN　is　deployed　to　design　the　optimal　structural　material　distribution　together　with　the　corresponding　continuous　fiber　orientations.　Numerical　simulations　for　benchmark　structure　verify　that　the　proposed　method　greatly　improves　the　design　efficiency　of　CFRCS　along　with　high　design　accuracy.　Furthermore,　the　CFRCS　topology　configuration　designed　by　ResUNet-GAN　is　fabricated　by　additive　manufacturing.　Compression　experiments　of　the　specimens　show　that　both　the　stiffness　structure　and　peak　load　of　the　CFRCS　topology　configuration　designed　by　the　proposed　method　have　significantly　enhanced.　The　proposed　deep　learning-based　topology　optimization　method　will　provide　great　flexibility　in　CFRCS　for　engineering　applications.　©　The　Chinese　Society　of　Theoretical　and　Applied　Mechanics　and　Springer-Verlag　GmbH　Germany,　part　of　Springer　Nature　2024.