The　inherent　variability　and　unpredictability　in　open　multi-view　learning　scenarios　infuse　considerable　ambiguity　into　the　learning　and　decision-making　processes　of　predictors.　This　demands　that　predictors　not　only　recognize　familiar　patterns　but　also　adaptively　interpret　unknown　ones　out　of　training　scope.　To　address　this　challenge,　we　propose　an　Ambiguity-Aware　Multi-view　Learning　Framework,　which　integrates　four　synergistic　modules　into　an　end-to-end　framework　to　achieve　generalizability　and　reliability　beyond　the　known.　By　introducing　the　mixed　samples　to　broaden　the　learning　sample　space,　accompanied　by　corresponding　soft　labels　to　encapsulate　their　inherent　uncertainty,　the　proposed　method　adapts　to　the　distribution　of　potentially　unknown　samples　in　advance.　Furthermore,　an　instance-level　sparse　inference　is　implemented　to　learn　sparse　approximated　points　in　the　multiple　view　embedding　space,　and　individual　view　representations　are　gated　by　view-level　confidence　mappings.　Finally,　a　multi-view　consistent　representation　is　obtained　by　dynamically　assigning　weights　based　on　the　degree　of　cluster-level　dispersion.　Extensive　experiments　demonstrate　that　our　approach　is　effective　and　stable　compared　with　other　state-of-the-art　methods　in　open-world　recognition　situations.　©　2024　ACM.