Hyperspectral　image　(HSI)　classification　attempts　to　classify　each　pixel,　which　is　an　important　means　of　obtaining　land-cover　knowledge.　HSIs　are　cubic　data　with　spectral-spatial　knowledge　and　can　generally　be　considered　as　sequential　data　alongside　spectral　dimension.　Unlike　convolutional　neural　networks　(CNNs),　which　mainly　focus　on　local　relationship　models　in　images,　transformers　have　been　shown　to　be　a　powerful　structure　for　qualifying　sequence　data.　However,　it　lacks　the　excellent　ability　of　CNNs　in　establishing　local　relationships　in　images　and　cannot　perform　good　generalization　in　the　case　of　insufficient　data.　In　addition,　the　gradient　disappearance　problem　hinders　the　convergence　stability　of　deep　learning　networks　as　the　layers　get　deeper.　To　address　these　problems,　we　propose　a　Cascaded　Convolution-based　Transformer　with　Densely　Connected　Mechanism　(CDCformer)　for　HSI　classification.　First,　we　propose　a　cascaded　convolution　feature　tokenization　to　extract　spectral-spatial　information,　which　will　introduce　some　inductive　bias　properties　of　CNN　into　the　transformer.　In　addition,　we　design　a　simple　and　effective　densely　connected　transformer　to　enhance　feature　propagation　and　transfer　memorable　information　from　shallow　to　deep　layers.　It　efficiently　improves　the　performance　of　the　transformer　and　extracts　more　discriminative　spectral-spatial　features　from　the　HSI.　Extensive　experimental　evaluation　of　three　public　hyperspectral　datasets　shows　that　CDCformer　achieves　competitive　classification　results.