Depression　is　a　complex　mental　disease,　which　involves　many　factors　such　as　psychology,　physiology,　and　society,　and　which　causes　harm　to　society.　Up　to　now,　there　are　no　valuable　biomarkers　for　clinical　diagnosis.　This　research　constructed　a　dataset,　which　includes　calm,　sad,　and　happy　facial　expressions　from　both　patients　with　depression　and　normal　people,　and　classification　and　visualization　of　depression.　The　network　includes　a　dual-scale　convolution　module,　adaptive　channel　attentional　mechanism,　and　gradient　class　activation　mapping　technique.　In　which,　dual-scale　convolution　captures　features　of　the　facial　region　at　different　scales　and　the　adaptive　channel　attention　captures　the　facial　region　with　the　most　significant　features.　The　results　show　that　we　improve　the　performance　of　depression　classification　based　on　facial　information,　and　recruit　gradient　class　activation　mapping　technique　obtaining　a　specific　visual　face　pattern　of　depression　that　is　different　from　that　of　normal　people,　which　provides　a　potential　interpretable　and　discriminant　evidence　for　the　clinical　diagnosis.　Thereby,　promoting　the　development　and　application　of　artificial　intelligence　in　the　field　of　psychiatry.