This　work　proposes　a　research　scheme　to　speed　up　the　design　of　metasurface　skin　cloak　through　low-complexity　phase　monitoring　model　and　deep　learning.This　skin　cloak　conceals　a　three-dimensional　arbitrarily　shaped　object　by　complete　restoration　of　the　phase　of　the　reflected　light　at　specific　wavelength.　And　the　possibility　of　realizing　spectral　prediction　by　deep　learning　is　analyzed.During　the　study,　a　phase　monitoring　system　was　designed　in　which　the　detector,　the　light　source　and　the　monitored　nano-antenna　were　sequentially　distributed　at　equal　distances　from　the　emitted　wavelength　of　the　light　source,　so　that　the　monitored　phase　amount　was　exactly　equal　to　the　phase　change　before　the　reflected　wave,　thus　eliminating　the　need　for　multiple　monitors　to　measure　and　calculate　the　phase　change　before　and　after　the　reflection.The　traditional　metasurface　design　is　usually　constructed　by　manual　library　construction　based　on　the　phase　distribution　and　the　relationship　between　phase　variation　and　dimensional　variation　of　the　cell　structure,　so　this　work　combines　the　aforementioned　monitoring　model　with　deep　learning　to　generate　the　database　required　for　modeling.The　two　variable　parameters　of　device　length　and　width　were　first　defined,　and　the　reflected　wavefront　phase　change　used　as　the　optical　response,　and　we　reprocessed　the　original　data　and　finally　build　and　trained　an　artificial　neural　network　model　for　forward　prediction　of　optical　response.This　network　can　obtain　its　MSE　below　0.001　for　the　test　set　after　the　training　is　completed.　Thus　the　scheme　can　replace　the　role　of　simulation　software　to　some　extent,　and　its　prediction　process　can　be　completed　in　a　few　milliseconds,　improving　the　efficiency　of　the　design　metasurface　process.　©　2022　SPIE.