In　multi-view　representation　learning　(MVRL),　the　challenge　of　category　uncertainty　is　significant.　Existing　methods　excel　at　deriving　shared　representations　across　multiple　views,　but　often　neglect　the　uncertainty　associated　with　cluster　assignments　from　each　view,　thereby　leading　to　increased　ambiguity　in　the　category　determination.　Additionally,　methods　like　kernel-based　or　neural　network-based　approaches,　while　revealing　nonlinear　relationships,　lack　attention　to　category　uncertainty.　To　address　these　limitations,　this　paper　proposes　a　method　leveraging　the　uncertainty　of　label　distributions　to　enhance　MVRL.　Specifically,　our　approach　combines　uncertainty　reduction　based　on　label　distribution　with　view　representation　learning　to　improve　clustering　accuracy　and　robustness.　It　initially　computes　the　within-view　representation　of　the　sample　and　semantic　labels.　Then,　we　introduce　a　novel　constraint　based　on　either　variance　or　information　entropy　to　mitigate　class　uncertainty,　thereby　improving　the　discriminative　power　of　the　learned　representations.　Extensive　experiments　conducted　on　diverse　multi-view　datasets　demonstrate　that　our　method　consistently　outperforms　existing　approaches,　producing　more　accurate　and　reliable　class　assignments.　The　experimental　results　highlight　the　effectiveness　of　our　method　in　enhancing　MVRL　by　reducing　category　uncertainty　and　improving　overall　classification　performance.　This　method　is　not　only　very　interpretable　but　also　enhances　the　model＇s　ability　to　learn　multi-view　consistent　information.