Recently,　pseudolabel-based　deep　learning　methods　have　shown　excellent　performance　in　semi-supervised　hyperspectral　image　(HSI)　classification.　These　methods　usually　select　high-confidence　unlabeled　samples　to　help　optimize　backbone　classification　networks.　However,　a　large　number　of　remaining　low-confidence　unlabeled　samples,　which　contain　rich　land-covers　information,　are　underused.　In　this　article,　we　propose　a　pseudolabel-based　unreliable　sample　learning　(PUSL)　method　to　fully　exploit　low-confidence　unlabeled　samples　for　semi-supervised　HSI　classification.　First,　to　avoid　overfitting　the　spatial　distribution　of　labeled　samples,　we　build　a　position-free　transformer　(PFT)　as　the　backbone　classification　network.　Second,　PFT　is　initially　trained　with　labeled　samples　in　a　supervised　learning　manner　to　obtain　an　initial　classifier,　which　is　then　used　to　split　unlabeled　samples　into　reliable　and　unreliable　unlabeled　samples　based　on　the　predicted　confidence.　Third,　reliable　unlabeled　samples　participate　in　training　along　with　labeled　samples.　Finally,　unreliable　unlabeled　samples　are　treated　as　negative　samples　for　the　corresponding　categories　to　improve　the　discrimination　of　PFT　in　a　contrastive　learning　paradigm.　Extensive　experiments　on　three　HSI　datasets　demonstrate　that　PUSL　outperforms　the　compared　methods.