Data-driven　deep　learning　frameworks　have　significantly　advanced　the　development　of　modern　machine　learning,　and　after　achieving　great　success　in　the　field　of　image,　speech,　and　video　recognition　and　processing,　they　have　also　begun　to　permeate　other　disciplines　such　as　physics,　chemistry,　and　the　discovery　of　new　drugs　and　new　materials.　Our　work　proposes　a　deep　learning-based　model　consisting　of　two　parts:　a　forward　simulation　network　that　contains　a　transposed　convolutional　network,　up　and　down　sampling　blocks　and　dense　layers　can　rapidly　predict　optical　responses　from　metasurface　structures,　and　an　inverse　design　network　that　contains　convolutional　neural　networks　and　dense　layers　can　automatically　construct　metasurface　based　on　the　input　optical　responses.　Our　model　assists　in　discovering　the　complex　and　non　-intuitive　relationship　between　the　moth-eye　metasurface　and　optical　responses,　and　designs　a　metasurface　with　excellent　optical　properties　(ultra-broadband　anti-reflection　or　nonlinear　function　of　reflectivity),　while　avoiding　traditional　time-consuming　case-by-case　numerical　simulations　in　the　metasurface　design.　This　work　provides　a　fast,　practical,　and　robust　method　to　study　complex　light-matter　interactions　and　to　accelerate　the　demand-based　design　of　nanophotonic　devices,　opening　a　new　avenue　for　the　development　of　real　nanophotonic　applications.