Multi-view　learning　exploits　the　complementary　nature　of　multiple　modalities　to　enhance　performance　across　diverse　tasks.　While　deep　learning　has　significantly　advanced　these　fields　by　enabling　sophisticated　modeling　of　intra-view　and　cross-view　interactions,　many　existing　approaches　still　rely　on　heuristic　architectures　and　lack　a　principled　framework　to　capture　the　dynamic　evolution　of　feature　representations.　This　limitation　hampers　interpretability　and　theoretical　understanding.　To　address　these　challenges,　this　paper　introduces　the　Multi-view　State-Space　Model　(MvSSM),　which　formulates　multi-view　representation　learning　as　a　continuous-time　dynamical　system　inspired　by　control　theory.　In　this　framework,　view-specific　features　are　treated　as　external　inputs,　and　a　shared　latent　representation　evolves　as　the　internal　system　state,　driven　by　learnable　dynamics.　This　formulation　unifies　feature　integration　and　label　prediction　within　a　single　interpretable　model,　enabling　theoretical　analysis　of　system　stability　and　representational　transitions.　Two　variants,　MvSSM-Lap　and　MvSSM-iLap,　are　further　developed　using　Laplace　and　inverse　Laplace　transformations　to　derive　system　dynamics　representations.　These　solutions　exhibit　structural　similarities　to　graph　convolution　operations　in　deep　networks,　supporting　efficient　feature　propagation　and　theoretical　interpretability.　Experiments　on　benchmark　datasets　such　as　IAPR-TC12,　and　ESP　demonstrate　the　effectiveness　of　the　proposed　method,　achieving　up　to　4.31　%　improvement　in　accuracy　and　4.27　%　in　F1-score　over　existing　state-of-the-art　approaches.　©　2025　Elsevier　Ltd