Accurate　identification　of　sentiments　in　government-related　comments　is　crucial　for　policymakers　to　deeply　understand　public　opinion,　adjust　policies　promptly,　and　enhance　overall　satisfaction.　Thus,　we　create　a　model　for　emotion　recognition　in　multimodal　sentiment　analysis　of　government　information　comments　based　on　contrastive　learning　and　cross-attention　fusion　networks.　Firstly,　we　collect　text-image　comments　from　Today＇s　Headlines　App＇s　Politics　and　Law　section　and　extract　textual　and　visual　features.　We　fine-tune　the　model　with　LoRA　and　optimize　the　feature　representation　by　making　low-rank　adjustments　to　the　fused　features.　Secondly,　we　utilize　contrastive　learning　with　reverse　prediction　to　analyze　intra-class　and　inter-class　cross-modal　dynamics.　Then,　we　propose　a　novel　fusion　network　that　utilizes　cross-attention　to　learn　the　complementary　relationship　between　different　modalities.　Finally,　the　features　are　combined　using　the　fully　connected　layer.　The　experiment　illustrates　that　the　model　achieves　a　96.80%　accuracy　in　recognizing　emotion　polarity.　Compared　with　the　multimodal　model　CLIP,　the　accuracy　of　the　proposed　method　is　improved　by　10.21%.　The　model　could　assist　the　government　in　emotional　evolution　analysis,　detection　of　public　opinion,　and　online　public　opinion　guidance.