Aiming　at　speeding　up　the　process　of　topology　optimization　and　generating　a　novel　high-quality　topology　configuration,　a　topology　optimization　design　with　lightweight　structure　based　on　deep　learning　is　investigated.　First,　Independent　Continuous　Mapping　(ICM)　method　is　employed　to　establish　the　original　configurations　including　the　intermediate　configurations　and　the　corresponding　final　configurations,　and　the　original　configurations　are　padded　and　merged　to　form　the　dataset　required　by　the　deep　learning　network.　Then　the　high-dimensional　mapping　relationship　between　the　intermediate　configuration　and　the　final　configuration　is　created　by　using　Pix2pix　neural　network　(Pix2pix　NN),　which　transforms　the　topology　optimization　into　image-to-image　translation　problem.　Finally,　the　acceleration　algorithm　is　utilized　to　accelerate　the　iteration　process　by　the　pre-trained　network.　Numerical　examples　show　that　the　coupling　method　is　feasible　and　efficient　in　topology　optimization　design.　The　method　provides　a　new　solution　for　topology　optimization　design　to　shorten　the　iteration　process　and　broaden　the　application　of　ICM　method.