In　recent　years,　deep　learning　has　been　widely　applied　to　hyperspectral　image　(HSI)　classification　(HIC)　with　great　success.　However,　since　labeling　hyperspectral　images　(HSIs)　is　time-consuming　and　labor-intensive,　a　limited　number　of　labeled　HSIs　are　available,　making　it　difficult　to　train　feature　extractors　and　classifiers.　To　address　this　challenge,　this　article　introduces　a　self-supervised　spectral-spatial　graph　prototypical　network　(S4GPN)　for　few-shot　HIC.　In　addition,　we　combine　self-supervised　learning　(SSL)　with　few-shot　learning　(FSL)　to　provide　additional　semantic　information　to　improve　classification　accuracy.　Our　method　consists　of　three　stages,　including　the　prototypical　network　(PN)　stage,　the　SSL　stage,　and　the　fusion　stage,　with　each　stage　progressively　improving　the　classification　performance.　In　the　PN　stage,　we　perform　supervised　learning　using　a　PN　structure　and　leverage　the　supervised　information　to　guide　SSL.　In　the　SSL　stage,　we　use　the　SimSiam　structure　in　a　novel　way　for　model　training　after　the　augmentation　of　spectral　and　spatial　data　separately.　Finally,　the　features　learned　in　the　first　two　stages　are　fused　to　improve　the　quality　of　the　feature　representation.　These　three　stages　use　the　same　structured　feature　extractors.　To　extract　more　diverse　and　discriminative　feature　representations　for　the　HIC　task,　our　method　uses　the　graph　convolution　network　(GCN)　and　the　dense　network　(Densenet)　to　extract　spectral　information　and　spatial　information,　respectively.　Experiments　on　four　datasets　and　comparisons　with　the　state-of-the-art　methods　demonstrate　that　our　proposed　S4GPN　outperforms　other　methods　for　HIC.