Human-computer　interaction　is　the　way　in　which　humans　and　machines　communicate　information.　With　the　rapid　development　of　deep　learning　technology,　the　technology　of　human-computer　interaction　has　also　made　a　corresponding　breakthrough.　In　the　past,　the　way　human-computer　interaction　was　mostly　relied　on　hardware　devices.　Through　the　coordinated　work　of　multiple　sensors,　people　and　machines　can　realize　information　interaction.　However,　as　theoretical　technology　continues　to　mature,　algorithms　for　human-computer　interaction　are　also　being　enriched.　The　popularity　of　convolutional　neural　networks　has　made　image　processing　problems　easier　to　solve.　Therefore,　real-time　human-computer　interaction　can　be　performed　by　using　image　processing,　and　intelligent　of　human-computer　interaction　can　be　realized.　The　main　idea　of　this　paper　is　to　use　the　real-time　capture　of　face　images　and　video　information　to　image　the　face　image　information.　We　perform　feature　point　positioning　based　on　the　feature　points　of　the　face　image.　We　perform　expression　recognition　based　on　the　feature　points　that　are　located.　At　the　same　time,　we　perform　ray　tracing　for　the　identified　human　eye　area.　The　feature　points　of　the　face　and　the　corresponding　expressions　and　implementation　movements　represent　the　user＇s　use　appeal.　Therefore,　we　can　analyze　the　user＇s　use　appeal　by　locating　the　face　feature　area.　We　define　the　corresponding　action　information　for　specific　user　face　features.　We　extract　the　user＇s　corresponding　information　according　to　the　user＇s　face　features,　and　perform　human-computer　interaction　according　to　the　user＇s　information.　(C)　2020　Elsevier　Inc.　All　rights　reserved.