Transformer　has　achieved　outstanding　performance　in　many　fields　such　as　computer　vision　benefiting　from　its　powerful　and　efficient　modelling　ability　and　long-range　feature　extraction　capability　complementary　to　convolution.　However,　on　the　one　hand,　the　lack　of　CNN＇s　innate　inductive　biases,　such　as　translation　invariance　and　local　sensitivity,　makes　Transformer　require　more　data　for　learning.　On　the　other　hand,　labelled　hyperspectral　samples　are　scarce　due　to　the　time-consuming　and　costly　annotation　task.　To　this　end,　we　propose　a　semi-supervised　hierarchical　Transformer　model　for　HSI　classification　to　improve　the　classification　performance　of　the　Transformer　with　limited　labelled　samples.　In　order　to　perturb　the　samples　more　fully　and　extensively　to　improve　the　model　performance,　two　different　data　augmentation　methods　are　used　to　perturb　the　unlabelled　samples,　and　two　sets　of　augmented　samples　are　obtained　respectively.　The　pseudo-label　obtained　on　the　original　unlabelled　sample　is　used　to　simultaneously　supervise　the　augmented　sample　obtained　on　this　unlabelled　sample.　Among　them,　only　the　pseudo-labels　above　the　threshold　are　retained.　To　further　improve　the　model　stability　and　classification　accuracy,　hierarchical　patch　embedding　is　proposed　to　eliminate　the　mutual　interference　between　pixels.　Extensive　experiments　on　three　well-known　hyperspectral　datasets　validate　the　effectiveness　of　the　proposed　semi-supervised　Transformer　model.　The　experiments　show　that　the　model　achieves　excellent　classification　accuracy　even　when　there　are　only　10　labelled　samples　in　each　category,　which　can　effectively　improve　the　classification　performance　of　Transformer　under　small-scale　labelled　samples.