Accurate　histopathology　classification　is　a　crucial　factor　in　the　diagnosis　and　treatment　of　Cholangiocarcinoma　(CCA).　Hyperspectral　images　(HSI)　provide　rich　spectral　information　than　ordinary　RGB　images,　making　them　more　useful　for　medical　diagnosis.　The　Convolutional　Neural　Network　(CNN)　is　commonly　employed　in　hyperspectral　image　classification　due　to　its　remarkable　capacity　for　feature　extraction　and　image　classification.　However,　many　existing　CNN-based　HSI　classification　methods　tend　to　ignore　the　importance　of　image　spatial　context　information　and　the　interdependence　between　spectral　channels,　leading　to　unsatisfied　classification　performance.　Thus,　to　address　these　issues,　this　paper　proposes　a　Spatial-Spectral　Joint　Network　(SSJN)　model　for　hyperspectral　image　classification　that　utilizes　spatial　self-attention　and　spectral　feature　extraction.　The　SSJN　model　is　derived　from　the　ResNet18　network　and　implemented　with　the　non-local　and　Coordinate　Attention　(CA)　modules,　which　extract　long-range　dependencies　on　image　space　and　enhance　spatial　features　through　the　Branch　Attention　(BA)　module　to　emphasize　the　region　of　interest.　Furthermore,　the　SSJN　model　employs　Conv-LSTM　modules　to　extract　long-range　dependencies　in　the　image　spectral　domain.　This　addresses　the　gradient　disappearance/explosion　phenom-ena　and　enhances　the　model　classification　accuracy.　The　experimental　results　show　that　the　proposed　SSJN　model　is　more　efficient　in　leveraging　the　spatial　and　spectral　information　of　hyperspectral　images　on　multidimensional　microspectral　datasets　of　CCA,　leading　to　higher　classification　accuracy,　and　may　have　useful　references　for　medical　diagnosis　of　CCA.