Due　to　the　differences　in　visual　systems　between　children　and　adults,　a　professional　stereoscopic　3D　video　may　not　be　comfortable　for　children.　In　this　paper,　we　aim　to　answer　whether　a　stereoscopic　video　is　comfortable　for　children　to　watch　by　solving　the　visual　comfort　classification　for　stereoscopic　videos.　In　particular,　we　propose　a　two-stream　recurrent　neural　network　(RNN)　with　multi-level　attention　for　the　visual　comfort　classification　for　stereoscopic　videos.　Firstly,　we　propose　a　two-stream　RNN　to　extract　and　fuse　spatial　and　temporal　features　from　video　frames　and　disparity　maps.　Furthermore,　we　propose　using　multi-level　attention　to　effectively　enhance　the　features　in　frame　level,　shot　level,　and　finally　video　level.　In　addition,　to　our　best　knowledge,　we　establish　the　first　high-definition　stereoscopic　3D　video　dataset　for　performance　evaluation.　Experimental　results　show　that　our　proposed　model　can　effectively　classify　professional　stereoscopic　videos　into　visually　comfortable　for　children　or　adults　only.　©　2022　ACM.