Semi-supervised　classification　aims　to　leverage　a　small　amount　of　labeled　data　for　learning　tasks.　Multi-view　semi-supervised　classification　has　attracted　widespread　attention　because　it　can　exploit　multi-view　data　to　optimize　the　classification　performance.　However,　its　methods　are　often　ineffective　when　facing　extremely　limited　labeled　samples.　In　this　paper,　we　propose　a　novel　multi-view　semi-supervised　classification　model　via　auto-weighted　submarkov　random　walk.　The　proposed　method　can　utilize　similar　nodes,　spread　information　among　nodes　on　graphs　and　exploit　multi-view　data　with　less　labeled　information.　Accordingly,　it　enables　an　effective　exploitation　of　both　a　small　number　of　labeled　data　and　a　large　amount　of　unlabeled　data　by　connecting　them　to　designed　auxiliary　nodes.　Furthermore,　an　ideal　weight　on　the　Hellinger　distance　is　allocated　to　each　view　data　for　obtaining　a　global　label　indicator　matrix,　which　is　expected　to　be　robust　to　imbalanced　classes.　Compared　with　existing　state-of-the-art　methods,　extensive　experiments　on　six　widely　used　datasets　are　conducted　to　verify　the　superiority　of　the　proposed　method.