In　this　paper,　a　data-driven　topology　optimization　(TO)　method　is　proposed　for　the　efficient　design　of　three-dimensional　heat　transfer　structures.　The　presented　method　is　composed　of　four　parts.　Firstly,　the　three-dimensional　heat　transfer　topology　optimization　(HTTO)　dataset,　composed　of　both　design　parameters　and　the　corresponding　HTTO　configuration,　is　established　by　the　solid　isotropic　material　with　penalization　(SIMP)　method.　Secondly,　a　high-performance　surrogate　model,　named　ResUNet-assisted　generative　adversarial　nets　(ResUNet-GAN),　is　developed　by　combining　ReUNet　and　generative　and　adversarial　nets　(GAN).　Thirdly,　the　same-resolution　(SR)　ResUNet-GAN　is　deployed　to　design　three-dimensional　heat　transfer　configurations　by　feeding　design　parameters.　Finally,　the　finite　element　mesh　of　the　optimized　configuration　is　refined　by　the　cross-resolution　(CR)　ResUNet-GAN　to　obtain　near-optimal　three-dimensional　heat　transfer　configurations.　Compared　with　conventional　TO　methods,　the　proposed　method　has　two　outstanding　advantages:　(1)　the　developed　surrogate　model　establishes　the　end-to-end　mapping　from　the　design　parameters　to　the　three-dimensional　configuration　without　any　need　for　optimization　iterations　and　finite　element　analysis;　(2)　both　the　SR　ResUNet-GAN　and　the　CR　ResUNet-GAN　can　be　employed　individually　or　in　combination　to　achieve　each　function,　according　to　the　needs　of　heat　transfer　structures.　The　data-driven　method　provides　an　efficient　design　framework　for　three-dimensional　practical　engineering　problems.