The　convolutional　neural　network　(CNN)　is　a　feedforward　neural　network　with　deep　structure　and　convolution　operation.　In　the　hyperspectral　image　(HSI)　classification,　CNN　has　demonstrated　excellent　performance　in　extracting　spectral　and　spatial　information.　However,　the　inherent　complexity　and　high　dimension　of　HSIs　still　limit　the　performance　of　most　neural　network　models.　The　powerful　feature　extraction　ability　of　CNN　is　normally　achieved　by　dozens　or　more　layers,　which　brings　a　series　of　problems　such　as　gradient　vanishing,　overfitting,　and　slow　training　speed.　In　order　to　address　these　problems,　this　article　presents　a　CNN　architecture-based　stochastic　depth　residual　network　(SDRN),　which　is　specially　designed　for　HSI　data.　This　model　takes　the　original　3-D　cube　as　the　input　and　3-D　convolution　is　used　to　extract　abundant　spectral　and　spatial　features　through　corresponding　residual　blocks.　In　order　to　reduce　the　training　time,　we　adopt　a　stochastic　depth　strategy.　For　each　small　batch,　a　sublayer　is　randomly　discarded　by　an　identity　function.　During　the　testing　stage,　the　residual　network　with　complete　depth　is　used.　Experiments　on　three　datasets　and　a　comparison　of　the　state-of-art　methods　show　that　SDRN　has　great　advantages　in　accuracy　and　training　time　compared　with　state-of-the-art　HSI　classification　methods.