Depression　is　a　serious　mental　disorder,　significantly　burdens　individuals,　families,　and　society.　For　clinical　psychiatrists,　assessing　the　severity　of　depression　is　a　crucial　tool　in　selecting　treatment　approaches　and　evaluating　their　effectiveness.　Although　many　studies　in　machine　learning　have　focused　on　the　automatic　evaluation　of　self-rating　scales　[such　as　the　Beck　Depression　Inventory-II　(BDI-II)　and　the　Patient　Health　Questionnaire-8　(PHQ-8)],　research　into　the　machine　learning-based　automatic　evaluation　of　the　medical　clinical　assessment　scale　[such　as　the　Hamilton　Depression　Scale　(HAMD)]　has　not　yet　been　focused　on.　In　this　study,　an　end-to-end　automatic　evaluation　device　for　HAMD　and　Patient　Health　Questionnaire-9　(PHQ-9)　scores　was　developed.　In　addition,　we　constructed　a　dataset　consisting　of　emotional　facial　expression　videos　(eFEVs)　signals　and　emotional　pupil-wave　(ePW)　signals　from　65　patients　with　depression.　The　dataset　has　HAMD　and　PHQ-9　score　labels,　encompassing　two　emotional　states:　sadness　and　happiness.　We　built　a　3-dimensional　convolutional　neural　network　+　long　short-term　memory　(3DCNN　+　LSTM)　model　framework　and　a　multiscale　1-dimensional　convolutional　neural　network　(1DCNN)　to　learn　and　extract　features　from　eFEVs　and　ePW　automatically.　The　results　showed　that　compared　with　the　previous　evaluation　methods　for　depression　levels,　the　evaluation　precision　of　HAMD　and　PHQ-9　has　been　improved　significantly.　The　results　also　showed　that,　in　both　HAMD　and　PHQ-9　evaluations,　the　evaluation　precision　of　eFEVs　was　superior　to　ePW,　and　HAMD　is　better　than　PHQ-9.　These　studies　indicated　that　both　emotional　facial　expressions　and　ePW　can　better　represent　depressive　mood　in　patients　with　depression,　especially　emotional　facial　expressions,　and　the　predictive　precision　of　the　medical　scale　is　significantly　better　than　the　self-rating　scale.　This　automated　assessment　method　and　device　can　assist　doctors　in　diagnosing　depressive　symptoms　more　effectively　and　serve　as　an　evaluation　tool　for　treatment　efficacy.