Multi-view　learning　methods　leverage　multiple　data　sources　to　enhance　perception　by　mining　correlations　across　views,　typically　relying　on　predefined　categories.　However,　deploying　these　models　in　real-world　scenarios　presents　two　primary　openness　challenges.　1)　Lack　of　Interpretability:　The　integration　mechanisms　of　multi-view　data　in　existing　black-box　models　remain　poorly　explained;　2)　Insufficient　Generalization:　Most　models　are　not　adapted　to　multi-view　scenarios　involving　unknown　categories.　To　address　these　challenges,　we　propose　OpenViewer,　an　openness-aware　multi-view　learning　framework　with　theoretical　support.　This　framework　begins　with　a　Pseudo-Unknown　Sample　Generation　Mechanism　to　efficiently　simulate　open　multi-view　environments　and　previously　adapt　to　potential　unknown　samples.　Subsequently,　we　introduce　an　Expression-Enhanced　Deep　Unfolding　Network　to　intuitively　promote　interpretability　by　systematically　constructing　functional　prior-mapping　modules　and　effectively　providing　a　more　transparent　integration　mechanism　for　multi-view　data.　Additionally,　we　establish　a　Perception-Augmented　Open-Set　Training　Regime　to　significantly　enhance　generalization　by　precisely　boosting　confidences　for　known　categories　and　carefully　suppressing　inappropriate　confidences　for　unknown　ones.　Experimental　results　demonstrate　that　OpenViewer　effectively　addresses　openness　challenges　while　ensuring　recognition　performance　for　both　known　and　unknown　samples.　Copyright　©　2025,　Association　for　the　Advancement　of　Artificial　Intelligence　(www.aaai.org).　All　rights　reserved.