The　facial　expression　is　diverse　and　various　among　persons　due　to　the　impact　of　the　psychology　factor.　Whilst　the　facial　action　is　comparatively　steady　because　of　the　fixedness　of　the　anatomic　structure.　Therefore,　to　improve　performance　of　the　action　unit　recognition　will　facilitate　the　facial　expression　recognition　and　provide　profound　basis　for　the　mental　state　analysis,　etc.　However,　it　still　a　challenge　job　and　recognition　accuracy　rate　is　limited,　because　the　muscle　movements　around　the　face　are　tiny　and　the　facial　actions　are　not　obvious　accordingly.　Taking　account　of　the　moving　of　muscles　impact　each　other　when　person　express　their　emotion,　we　propose　to　make　full　use　of　co-occurrence　relationship　among　action　units　(AUs)　in　this　paper.　Considering　the　dynamic　characteristic　of　AUs　as　well,　we　adopt　the　3D　Convolutional　Neural　Network(3DCNN)　as　base　framework　and　proposed　to　recognize　multiple　action　units　around　brows,　nose　and　mouth　specially　contributing　in　the　emotion　expression　with　putting　their　co-occurrence　relationships　as　constrain.　The　experiments　have　been　conducted　on　a　typical　public　dataset　CASME　and　its　variant　CASME2　dataset.　The　experiment　results　show　that　our　proposed　AU　co-occurrence　constraint　3DCNN　based　AU　recognition　approach　outperforms　current　approaches　and　demonstrate　the　effectiveness　of　taking　use　of　AUs　relationship　in　AU　recognition.