Ground-glass　nodules　(GGN)　are　the　main　manifestation　of　early　lung　cancer,　and　accurate　and　efficient　identification　of　ground-glass　pulmonary　nodules　is　of　great　significance　for　the　treatment　of　lung　diseases.　In　response　to　the　problem　of　traditional　machine　learning　requiring　manual　feature　extraction,　and　most　deep　learning　models　applied　to　2D　image　classification,　this　paper　proposes　a　Transformer-based　recognition　model　for　ground-glass　nodules　from　the　view　of　global　3D　asymmetry　feature　representation.　Firstly,　a　3D　convolutional　neural　network　is　used　as　the　backbone　to　extract　the　features　of　the　three-dimensional　CT-image　block　of　pulmonary　nodules　automatically;　secondly,　positional　encoding　information　is　added　to　the　extracted　feature　map　and　input　into　the　Transformer　encoder　layer　for　further　extraction　of　global　3D　asymmetry　features,　which　can　preserve　more　spatial　information　and　obtain　higher-order　asymmetry　feature　representation;　finally,　the　extracted　asymmetry　features　are　entered　into　a　support　vector　machine　or　ELM-KNN　model　to　further　improve　the　recognition　ability　of　the　model.　The　experimental　results　show　that　the　recognition　accuracy　of　the　proposed　method　reaches　95.89%,　which　is　4.79,　2.05,　4.11,　and　2.74　percentage　points　higher　than　the　common　deep　learning　models　of　AlexNet,　DenseNet121,　GoogLeNet,　and　VGG19,　respectively;　compared　with　the　latest　models　proposed　in　the　field　of　pulmonary　nodule　classification,　the　accuracy　has　been　improved　by　2.05,　2.05,　and　0.68　percentage　points,　respectively,　which　can　effectively　improve　the　recognition　accuracy　of　ground-glass　nodules.