With　the　rapid　development　of　e-commerce　platforms,　how　to　better　identify　and　filter　fake　reviews　has　become　an　urgent　issue　for　the　healthy　development　of　the　e-commerce　industry.　However,　traditional　fake　review　identification　methods　cannot　effectively　solve　this　problem.　They　are　easily　affected　by　text　changes,　language　differences,　and　context,　and　fake　reviewers　may　take　measures　to　blur　their　behavioral　characteristics,　making　them　difficult　to　detect　by　behavior-based　algorithms.　Large　language　models　can　capture　the　contextual　relationships　of　the　entire　text　through　self-attention　mechanisms,　thereby　understanding　the　overall　meaning　and　emotional　tendency　of　the　review,　which　enables　them　to　more　effectively　judge　the　authenticity　of　the　review.　In　addition,　large　models　have　strong　generalization　ability　and　a　certain　degree　of　interpretability,　which　also　makes　them　suitable　for　research　on　fake　review　text　identification.　Since　fake　reviews　have　an　asymmetric　distribution　feature,　this　paper　uses　the　RoBERTa　model　to　extract　information　and　combine　it　with　the　behavioral　features　in　traditional　research　for　model　training.　Compared　with　traditional　methods,　the　accuracy　rate　is　improved　by　nearly　3%.　©　2024　The　Authors.