Domain　adaptation　is　a　fundamental　research　field,　which　focuses　on　transforming　knowledge　between　different　domains.　With　the　massive　growth　of　video　data,　the　video　domain　adaptation　problem　becomes　increasingly　significant　for　practical　tasks.　Motivated　by　the　excellent　performance　of　Grassmann　manifolds　representation　in　video　recognition　tasks,　we　propose　an　optimal　transport　based　video　domain　adaptation　model　on　Grassmann　manifolds.　The　proposed　model　reduces　the　discrepancy　between　different　domains　for　the　frame　and　video　level　features.　First,　the　frame　level　discrepancy　is　reduced　by　extracting　domain　consistency　features.　At　the　video　level,　a　fixed　number　of　frame　features　are　formed　and　represented　as　points　on　Grassmann　manifolds.　These　points　are　fused　with　predicted　labels　to　form　fusion　features.　Finally,　the　video　level　discrepancy　is　reduced　by　minimizing　the　distribution　discrepancy　of　the　fusion　features　between　two　domains.　Cross-domain　video　recognition　experiments　demonstrate　the　validity　of　the　proposed　model.　The　experimental　results　demonstrate　the　excellent　performance　of　the　proposed　algorithm　compared　with　the　state-of-art　video　domain　adaptation　models.　©　2022　Elsevier　Inc.