Pseudo-label　(PL)　learning-based　methods　usually　regard　class　confidence　above　a　certain　threshold　for　unlabeled　samples　as　PLs,　which　may　result　in　PLs　still　containing　wrong　labels.　In　this　letter,　we　propose　a　prototype-based　PL　refinement　(PPLR)　for　semi-supervised　hyperspectral　image　(HSI)　classification.　The　proposed　PPLR　filters　wrong　labels　from　PLs　using　class　prototypes,　which　can　improve　the　discrimination　of　the　network.　First,　PPLR　uses　multihead　attentions　(MHAs)　to　extract　the　spectral-spatial　features,　and　designs　an　adaptive　threshold　that　can　be　dynamically　adjusted　to　generate　high-confidence　PLs.　Then,　PPLR　constructs　class　prototypes　for　different　categories　using　labeled　sample　features　and　unlabeled　sample　features　with　refined　PLs　to　improve　the　quality　of　PLs　by　filtering　wrong　labels.　Finally,　PPLR　further　assigns　reliable　weights　(RWs)　to　these　PLs　in　calculating　their　supervised　loss,　and　introduces　a　center　loss　(CL)　to　improve　the　discrimination　of　features.　When　ten　labeled　samples　per　category　are　utilized　for　training,　PPLR　achieves　the　overall　accuracies　of　82.11%,　86.70%,　and　92.50%　on　the　Indian　Pines　(IP),　Houston2013,　and　Salinas　datasets,　respectively.