Quantum-inspired　models　have　demonstrated　superior　performance　in　many　downstream　language　tasks,　such　as　question　answering　and　sentiment　analysis.　However,　recent　models　primarily　focus　on　embedding　and　measurement　operations,　overlooking　the　significance　of　the　quantum　evolution　process.　In　this　work,　we　present　a　novel　quantum-inspired　neural　network,　LI-QiLM,　which　integrates　the　Lindblad　Master　Equation　(LME)　to　model　the　evolution　process　and　the　interferometry　to　the　measurement　process,　providing　more　physical　meaning　to　strengthen　the　interpretability.　We　conduct　comprehensive　experiments　on　six　sentiment　analysis　datasets.　Compared　to　the　traditional　neural　networks,　transformer-based　pre-trained　models　and　quantum-inspired　models,　such　as　CICWE-QNN　and　ComplexQNN,　the　proposed　method　demonstrates　superior　performance　in　accuracy　and　F1-score　on　six　commonly　used　datasets　for　sentiment　analysis.　Additional　ablation　tests　verify　the　effectiveness　of　LME　and　interferometry.　©　2024　Association　for　Computational　Linguistics.