While　deep　learning　has　been　widely　used　in　the　hyperspectral　image　(HSI)　classification,　lacking　labeled　HSI　poses　significant　challenges　to　effective　and　sufficient　learning.　To　address　this　issue,　this　letter　introduces　a　prototypical　network　with　residual　capsule　(PN-ResCapsNet)　for　few-shot　HSI　classification.　Compared　with　the　convolutional　neural　networks　(CNNs),　the　capsule　networks　can　better　capture　spatial　relationships.　To　better　extract　HSI　features,　residual　structures　and　self-attention　(SE)　mechanisms　are　incorporated,　which　can　overcome　the　limitation　of　shallow　feature　extraction　in　capsule　networks.　Moreover,　a　bias-reduction　(BR)　method,　consisting　of　an　interclass　BR　module　and　an　intraclass　BR　module,　is　designed　to　rectify　the　prototypes,　which　can　mitigate　the　bias　between　the　support　and　the　query　set　and　alleviate　the　bias　between　the　calculated　prototype　and　the　expected　prototype.　Experiments　on　widely　used　HSI　datasets　illustrate　that　the　proposed　method　outperforms　several　state-of-the-art　methods　and　achieves　the　overall　accuracies　of　92.07%,　81.02%,　and　85.35%　on　Kennedy　Space　Center　(KSC),　Houston　(HT),　and　Pavia　University　(PU)　datasets,　respectively.