In　the　task　of　multiview　multilabel　(MVML)　classification,　each　object　is　described　by　several　heterogeneous　view　features　and　annotated　with　multiple　relevant　labels.　Existing　MVML　methods　usually　assume　that　these　heterogeneous　features　are　strictly　view-aligned,　and　they　directly　conduct　cross-view　information　fusion　to　train　a　multilabel　prediction　model.　However,　in　real-world　scenarios,　such　strict　view-aligned　requirement　can　be　hardly　satisfied　due　to　the　recurrent　spatiotemporal　asynchronism　when　collecting　MVML　data,　which　would　cause　inaccurate　multiview　fusion　results　and　degrade　the　classification　performance.　To　address　this　issue,　we　propose　a　generalized　nonaligned　MVML　(GNAM)　classification　method,　which　achieves　multiview　information　fusion　while　aligning　cross-view　features　and　accordingly　learns　a　desired　multilabel　classifier.　Specifically,　we　first　introduce　a　multiorder　matching　alignment　strategy　to　achieve　cross-view　feature　alignments,　where　both　first-order　feature　correspondence　and　second-order　structure　correspondence　are　jointly　integrated　to　guarantee　the　compactness　of　the　view-alignment　results.　Afterward,　a　commonality-and　individuality-based　multiview　fusion　structure　is　formulated　on　the　aligned-view　features　to　excavate　the　consistencies　and　complementarities　across　different　views,　which　leads　all　relevant　multiview　semantic　labels,　especially　rare　labels,　to　be　characterized　more　comprehensively.　Finally,　we　embed　adaptive　global　label　correlations　to　multilabel　classification　model　to　further　enhance　its　semantic　expression　integrity　and　develop　an　alternative　algorithm　to　optimize　the　whole　model.　Extensive　experimental　results　have　verified　that　GNAM　is　significantly　superior　to　other　state-of-the-art　methods.