At　present,　there　have　been　many　studies　on　the　methods　of　using　the　deep　learning　regression　model　to　assess　depression　level　based　on　behavioral　signals　(facial　expression,　speech,　and　language);　however,　the　research　on　the　assessment　method　of　anxiety　level　using　deep　learning　is　absent.　In　this　article,　pupil-wave,　a　physiological　signal　collected　by　Human　Computer　Interaction　(HCI)　that　can　directly　represent　the　emotional　state,　is　developed　to　assess　the　level　of　depression　and　anxiety　for　the　first　time.　In　order　to　distinguish　between　different　depression　and　anxiety　levels,　we　use　the　HCI　method　to　induce　the　participants＇　emotional　experience　through　three　virtual　reality　(VR)　emotional　scenes　of　joyful,　sad,　and　calm,　and　construct　two　differential　pupil-waves　of　joyful　and　sad　with　the　calm　pupil-wave　as　the　baseline.　Correspondingly,　a　dual-channel　fusion　depression　and　anxiety　level　assessment　model　is　constructed　using　the　improved　multi-scale　convolution　module　and　our　proposed　width-channel　attention　module　for　one-dimensional　signal　processing.　The　test　results　show　that　the　MAE/RMSE　of　the　depression　and　anxiety　level　assessment　method　proposed　in　this　article　is　3.05/4.11　and　2.49/1.85,　respectively,　which　has　better　assessment　performance　than　other　related　research　methods.　This　study　provides　an　automatic　assessment　technique　based　on　human　computer　interaction　and　virtual　reality　for　mental　health　physical　examination.　©　2023　Association　for　Computing　Machinery.