To　address　the　problem　that　mobile　phone　surface　defects　are　difficult　to　be　identified　accurately,　an　approach　for　mobile　surface　defects　recognition　called　SL-MSCNN　was　proposed,　which　combined　the　Sobel　operator,　logistic　loss　function　(LLF)　and　multi-scale　convolutional　neural　networks　(MSCNN).　First,　a　neighborhood　feature　enhancement　method　based　on　Sobel　was　constructed,　which　can　exclude　the　interference　of　unrelated　factors　of　image,　such　as　lighting　and　shadow.　Second,　a　defect　recognition　method　based　on　MSCNN　was　designed,　which　can　improve　the　recognition　accuracy　of　mobile　phone　surface　by　obtaining　multi-scale　information　of　mobile　phone　surface　image.　Meanwhile,　LLF　was　introduced,　which　speeded　up　the　detection　speed　of　training　by　reducing　the　probability　of　gradient　disappearance.　Results　show　that　the　proposed　defect　recognition　method,　compared　with　other　methods,　can　improve　the　practicability　of　the　actual　process　in　terms　of　recognition　accuracy　and　efficiency.　©　2023　Beijing　University　of　Technology.　All　rights　reserved.