Hyperspectral　image　classification　has　always　been　a　task　of　great　concern　in　the　field　of　remote　sensing.　Transformer　networks　have　demonstrated　their　powerful　ability　to　capture　global　relationships　and　have　achieved　success　in　image　analysis.　However,　when　faced　with　the　task　of　hyperspectral　image　classification,　conventional　image　preprocessing　methods　lead　to　the　loss　of　spectral　information,　failing　to　retain　complete　spectral-spatial　information.　To　better　solve　this　problem,　this　paper　proposes　an　Attention-Guided　CNN-Transformer　Hybrid　Network　(AGCTHN).　This　network　combines　the　strengths　of　Convolutional　Neural　Networks　(CNNs)　and　Transformer　networks　in　feature　extraction　while　exploring　the　spectral　and　spatial　information　of　large-scale　and　complex　remote　sensing　images.　A　multi-level　attention　guidance　mechanism　is　designed　from　shallow　to　deep　at　different　scales,　where　convolutional　features　guide　the　self-attention　module　of　the　Transformer　to　focus　on　key　details　in　the　spectral　bands　of　the　image.　Extensive　experiments　on　hyperspectral　image　classification　datasets　validate　the　effectiveness　of　the　proposed　AGCTHN.　The　experimental　results　illustrate　that　the　network　outperforms　state-of-the-art　methods　in　classification　accuracy.　©　2023　IEEE.