Quantum-inspired　models　have　shown　enhanced　capabilities　in　various　language　tasks,　including　question　answering　and　sentiment　analysis.　However,　current　complex-valued-based　models　primarily　focus　on　sentence　embedding,　overlooking　the　significance　of　the　quantum　evolution　process　and　the　extra　time　cost　incurred　by　complex　expressions.　In　this　work,　we　present　a　novel　quantum-inspired　neural　network,　SSS-QNN,　which　integrates　the　Stochastic　Liouville-von　Neumann　Equation　(SLE)　to　simulate　the　evolution　process　and　the　complex-valued　simple　recurrent　unit　(SRU)　to　reduce　the　time　cost,　offering　the　model　physical　meaning,　thus　enhancing　the　interpretability.　We　conduct　comprehensive　experiments　on　both　sentence-level　and　document-level　sentiment　classification　datasets.　Compared　to　traditional　models,　large　language　models,　and　quantum-inspired　models,　SSS-QNN　demonstrates　competitive　performance　in　accuracy　and　time　cost.　Additional　ablation　tests　verify　the　effectiveness　of　the　proposed　modules.　©　2024　IEEE.