Infrared　hyperspectral　is　susceptible　to　clouds,　and　accurately　identifying　whether　the　hyperspectral　infrared　sounder　data　is　polluted　by　clouds　is　of　great　significance　for　numerical　weather　prediction　and　atmospheric　parameter　inversion.　Since　the　complex　spectral　characteristics　of　clouds,　the　existing　spectral　threshold　methods　and　machine　learning　methods　have　the　difficulties　of　undetermined　threshold　and　clear　field　of　view　(FOV)　missed　and　false　detections.　In　order　to　improve　the　cloud　recognition　accuracy　of　infrared　hyperspectral　data,　three　end-to-end　cloud　detection　models　combining　deep　neural　network　(DNN)　and　convolutional　neural　network　(CNN)　and　long　short-term　memory　network　(LSTM)　are　proposed.　In　this　paper,　taking　the　High　Spectral　Infrared　Atmospheric　Sounder　(HIRAS)　equipped　with　Fengyun-3D　(FY-3D)　satellite　as　the　research　object,　based　on　the　same　platform　Moderate　Resolution　Spectral　Imager-II　(MERSI-II)　cloud　mask　(CLM)　product,　the　HIRAS　Cloud　dataset　is　established,　and　the　accuracy　test　and　qualitative　analysis　are　carried　out　by　using　the　test　datasets　and　Typhoon　Siamba,　July　3,　2022,　as　well　as　the　earth　observation　scene　under　the　conditions　of　ice　and　snow　surface.　The　test　datasets　analysis　results　show　that　the　cloud　detection　accuracy　of　CNN　and　CNN-LSTM　model　is　stable　at　0.96,　and　the　false　alarm　rate　of　cloud　is　0.035　and　0.036,　respectively,　and　the　detection　ability　of　DNN　model　is　slightly　inferior　to　the　former　two　in　the　same　hidden　layer,　with　an　accuracyof　0.94.　In　further　qualitative　research,　we　found　that　the　CNN-LSTM　model　has　high　accuracy　and　robustness　in　infrared　hyperspectral　cloud　detection,　and　the　detection　results　in　a　variety　of　surface　scenarios　are　consistent　with　the　actual　situation　of　whether　clouds　occur　in　the　FOV　of　the　instrument.　Compared　with　CLM　products,　it　can　better　identify　clear　ocean　scenes,　and　provide　fast　and　efficient　cloud　detection　reference　for　data　assimilation　systems.