The　advancement　of　generative　artificial　intelligence　technology　has　significantly　contributed　to　the　progress　in　various　fields.　However,　this　technological　development　has　also　inadvertently　facilitated　the　creation　and　widespread　dissemination　of　misinformation.　Prior　research　has　concentrated　on　addressing　grammatical　issues,　inflammatory　content,　and　other　pertinent　features　by　employing　deep　learning　models　to　characterize　and　model　deceptive　elements　within　fake　news　content.　These　approaches　not　only　are　lack　of　the　capability　to　assess　the　content　itself,　but　also　fall　short　in　elucidating　the　reasons　behind　the　model＇s　classification.　Based　on　the　above　problems,　we　propose　a　fine-grained　fake　news　detection　method　with　implicit　semantic　enhancement.　This　method　fully　utilizes　the　summarization　and　reasoning　capabilities　of　the　existing　generative　large　language　model.　The　method　employs　inference　based　on　major　events,　fine-grained　minor　events,　and　implicit　information　to　systematically　evaluate　the　authenticity　of　news　content.　This　method　strategically　leverages　the　full　potential　of　the　model　by　decomposing　tasks,　thereby　not　only　optimizing　its　proficiency　but　also　significantly　enhancing　its　prowess　in　capturing　instances　of　fake　news.　Simultaneously,　it　is　designed　to　be　interpretable,　providing　a　solid　foundation　for　detection.　With　its　inherent　ability,　this　method　not　only　ensures　reliable　identification　but　also　holds　vast　potential　for　diverse　applications.　©　2024　Science　Press.　All　rights　reserved.