In　the　task　of　multiview　multilabel　(MVML)　classification,　each　instance　is　represented　by　several　heterogeneous　features　and　associated　with　multiple　semantic　labels.　Existing　MVML　methods　mainly　focus　on　leveraging　the　shared　subspace　to　comprehensively　explore　multiview　consensus　information　across　different　views,　while　it　is　still　an　open　problem　whether　such　shared　subspace　representation　is　effective　to　characterize　all　relevant　labels　when　formulating　a　desired　MVML　model.　In　this　article,　we　propose　a　novel　label-driven　view-specific　fusion　MVML　method　named　L-VSM,　which　bypasses　seeking　for　a　shared　subspace　representation　and　instead　directly　encodes　the　feature　representation　of　each　individual　view　to　contribute　to　the　final　multilabel　classifier　induction.　Specifically,　we　first　design　a　label-driven　feature　graph　construction　strategy　and　construct　all　instances　under　various　feature　representations　into　the　corresponding　feature　graphs.　Then,　these　view-specific　feature　graphs　are　integrated　into　a　unified　graph　by　linking　the　different　feature　representations　within　each　instance.　Afterward,　we　adopt　a　graph　attention　mechanism　to　aggregate　and　update　all　feature　nodes　on　the　unified　graph　to　generate　structural　representations　for　each　instance,　where　both　intraview　correlations　and　interview　alignments　are　jointly　encoded　to　discover　the　underlying　consensuses　and　complementarities　across　different　views.　Moreover,　to　explore　the　widespread　label　correlations　in　multilabel　learning　(MLL),　the　transformer　architecture　is　introduced　to　construct　a　dynamic　semantic-aware　label　graph　and　accordingly　generate　structural　semantic　representations　for　each　specific　class.　Finally,　we　derive　an　instance-label　affinity　score　for　each　instance　by　averaging　the　affinity　scores　of　its　different　feature　representations　with　the　multilabel　soft　margin　loss.　Extensive　experiments　on　various　MVML　applications　have　verified　that　our　proposed　L-VSM　has　achieved　superior　performance　against　state-of-the-art　methods.　The　codes　are　available　at　https://gengyulyu.github.io/homepage/assets/codes/LVSM.zip.