Image-text　multimodal　sentiment　analysis　aims　to　predict　sentimental　polarity　by　integrating　visual　modalities　and　text　modalities.　The　key　to　solving　the　multimodal　sentiment　analysis　task　is　obtaining　high-quality　multimodal　representations　of　both　visual　and　textual　modalities　and　achieving　efficient　fusion　of　these　representations.　Therefore,　a　cross-modal　multi-level　fusion　sentiment　analysis　method　based　on　visual　language　model(MFVL)　is　proposed.　Firstly,　based　on　the　pre-trained　visual　language　model,　high-quality　multimodal　representations　and　modality　bridge　representations　are　generated　by　freezing　the　parameters　and　a　low-rank　adaptation　method　being　adopted　for　fine-tuning　the　large　language　model.　Secondly,　a　cross-modal　multi-head　co-attention　fusion　module　is　designed　to　perform　interactive　weighted　fusion　of　the　visual　and　textual　modality　representations　respectively.　Finally,　a　mixture　of　experts　module　is　designed　to　deeply　fuse　the　visual,　textual　and　modality　bridging　representations　to　achieve　multimodal　sentiment　analysis.　Experimental　results　indicate　that　MFVL　achieves　state-of-the-art　performance　on　the　public　evaluation　datasets　MVSA-Single　and　HFM.　©　2024　Science　Press.　All　rights　reserved.