Multi-view　learning　integrates　data　from　multiple　sources　to　enhance　task　performance　by　improving　data　quality.　However,　existing　approaches　primarily　focus　on　intra-distribution　data　learning　and　consequently　fail　to　identify　out-of-distribution　instances　effectively.　This　paper　introduces　a　method　to　improve　the　perception　of　out-of-distribution　data　in　multi-view　situations.　First,　we　employ　multi-view　consistency　and　complementarity　principles　to　develop　sub-view　complementarity　representation　learning　and　multi-view　consistency　fusion　layers,　thereby　enhancing　the　model＇s　perception　ability　to　typical　intra-distribution　features.　Additionally,　we　introduce　a　specialized　multi-view　training　loss　and　an　agent　mechanism　tailored　for　out-of-distribution　scenarios,　facilitating　the　ability　to　differentiate　between　known　and　new　or　anomalous　instances　effectively.　The　proposed　approach　enhances　the　recognition　of　out-of-distribution　data　by　improving　intradistribution　feature　representations　and　minimizing　the　entropy　associated　with　out-of-distribution　instances.　Experimental　results　on　multiple　multi-view　datasets　simulating　out-of-distribution　scenarios　confirm　the　effectiveness　of　MOLA,　which　consistently　outperforms　all　baselines　with　average　accuracy　improvements　of　over　5%.