The　rapid　spread　of　fake　news　on　social　media　has　a　great　impact　on　people＇s　lives　and　social　stability.　Existing　multimodal　fake　news　detection　methods　are　very　concerned　about　whether　the　cross-modal　information　matches.　In　order　to　better　understand　the　relationship　between　different　modalities,　this　paper　proposes　the　Contrastive　Learning　Based　on　Feature　Enhancement　model(CONLFE),　which　consists　of　three　core　modules:　the　High-quality　Feature　Extraction　based　on　CLIP　module,　the　Feature　Interaction　Based　on　Transformer　module　and　the　Cross-modal　Contrastive　Learning　module.　Firstly,　the　pre-train　CLIP　model　is　used　to　extract　richer　semantic　features　from　text　and　images.　Then,　the　attention　mechanism　in　transformer　is　used　to　process　and　optimize　the　interaction　between　enhanced　uni-modal　features.　Additionally,　by　learning　matched　and　mismatched　text-image　pairs　in　real　news,　the　representation　of　different　modalities　is　aligned　to　a　certain　extent.　This　method　effectively　improves　the　accuracy　and　efficiency　of　multimodal　fake　news　detection.　©　2024　Technical　Committee　on　Control　Theory,　Chinese　Association　of　Automation.