At　present,　the　main　research　object　of　facial　expression　recognition　is　2D　image;　it　does　not　have　enough　information,　and　is　vulnerable　to　the　face　pose,　illumination　and　etc.　Secondly,　the　facial　expression　recognition　methods　are　mostly　based　on　low-level　visual　features　of　the　image,　but　the　human　understanding　of　image　is　based　on　high-level　semantic　knowledge;　there　are　essential　differences　between　them,　i.　e.　the　＇semantic　gap＇.　So,　based　on　3D　facial　expression　image　and　semantic　knowledge,　a　3D　facial　expression　recognition　method　is　innovatively　proposed　based　on　bimodal　and　semantic　knowledge.　Firstly,　a　method　is　proposed,　which　carries　out　the　bimodal　fusion　of　3D　local　curvature　and　2D　local　corner;　and　the　method　can　extract　the　low-level　visual　features　of　3D　facial　expression　automatically.　Then　a　high-level　semantic　knowledge　vector　is　calculated　by　combining　AHP　and　G1.　Finally,　K-NN　algorithm　is　adopted　to　fuse　the　low-level　visual　features　and　high-level　semantic　knowledge,　narrow　the　＇semantic　gap＇　between　the　low-level　visual　features　and　high-level　semantic　knowledge,　and　increase　the　recognition　rate　of　facial　expression　recognition.