Quantum-inspired　models　have　demonstrated　superior　performance　in　many　downstream　language　tasks,　such　as　question　answering　and　sentiment　analysis.　However,　recent　models　have　primarily　focused　on　embedding　and　measurement　operations,　overlooking　the　significance　of　the　quantum　evolution　process.　In　this　work,　we　present　a　novel　quantum-inspired　neural　network,　LME-QNN,　which　integrates　the　Lindblad　Master　Equation　(LME)　to　model　the　evolution　process.　LME-QNN　is　able　to　capture　rich　correlations　among　words,　ensuring　a　potent　representation　of　the　sentence.　We　conduct　comprehensive　experiments　on　six　sentiment　analysis　datasets.　Compared　to　the　traditional　models,　including　TextCNN,　GRU,　ELMo,　BERT　and　RoBERTa,　and　quantum-inspired　models,　such　as　ComplexCNN　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.