With　the　rapid　proliferation　of　wireless　devices,　effectively　authenticating　legitimate　users　has　become　a　pivotal　challenge　in　wireless　communication.　Amongst　various　approaches,　physical　layer　authentication　technology　based　on　deep　learning　has　garnered　substantial　attention　from　numerous　researchers.　In　this　paper,　we　propose　a　scheme　for　implementing　physical　layer　authentication　based　on　the　Swin　Transformer,　utilizing　Channel　State　Information　(CSI)　to　distinguish　between　legitimate　and　illegitimate　nodes　in　industrial　network　systems.　In　contrast　to　traditional　physical　layer　authentication　methods　based　on　thresholds,　the　method　proposed　in　this　paper　eschews　the　use　of　thresholds　to　achieve　authentication.　Moreover,　compared　to　other　methods　based　on　deep　neural　networks,　the　introduction　of　attention　mechanisms　enables　superior　learning　of　wireless　channel　state　features,　enhancing　model　accuracy　and　reducing　computational　complexity.　The　efficacy　of　this　scheme　is　validated　through　channel　probing　results　in　typical　industrial　wireless　environments　provided　by　the　National　Institute　of　Standards　and　Technology　(NIST),　which　will　facilitate　the　application　of　deep　learning　technology　to　industrial　wireless　network　systems　to　enhance　their　security.　©　2023　ACM.