In　multimodal　sentiment　analysis,　the　primary　challenge　lies　in　effectively　modeling　the　complicated　interactions　among　different　data　modalities.　A　promising　approach　is　leveraging　quantum　concepts　like　superposition　and　entanglement　to　enhance　the　feature　representation　ability.　However,　existing　quantum-inspired　models　neglect　the　intricate　nonlinear　dynamics　inside　their　multimodal　components.　Drawing　inspiration　from　the　Lindbladian　concept　in　quantum　mechanics,　we　proposes　quantum-inspired　neural　network　with　the　Lindblad　Master　Equation　(LME)　and　complex-valued　LSTM.　The　proposed　model　treats　each　modality　as　an　individual　quantum　system　and　superposes　them　into　a　mixed　quantum　system.　The　trainable　LME　process　models　the　interaction　of　this　multimodal　system　with　its　semantic　environment,　thereby　enhancing　the　representation　of　complex　interactions　among　modalities.　The　efficacy　of　the　proposed　model,　along　with　its　key　components,　are　validated　through　extensive　experiments　on　the　MVSA　and　Memotion　datasets.　The　performance　are　complemented　by　a　comparative　analysis　that　benchmarks　the　model　against　state-of-the-art　methods,　including　traditional　methods,　large　language　models　and　quantum-insipred　methods.　Furthermore,　the　interpretability　of　the　model　is　enhanced　by　quantifying　the　entanglement　entropy　of　modality　combinations　using　the　von-Neumann　Entanglement　entropy.　©　2025　Elsevier　B.V.