Recent　researches　on　multiview　learning　have　received　widespread　attention　due　to　the　increasing　generalization　of　multiview　data.　As　an　effective　probabilistic　model,　random　walk　has　also　shown　encouraging　performance　in　various　fields.　To　further　exploit　the　potential　of　utilizing　random　walk　schemes　to　address　multiview　learning　problems,　this　article　proposes　a　simple　yet　efficient　multigraph　random　walk　scheme　for　both　multiview　clustering　and　semisupervised　classification　tasks.　The　proposed　model　integrates　random　walk　with　multiview　learning,　and　recursively　learns　a　globally　stable　probability　distribution　matrix　from　multiple　views,　on　the　basis　of　which　the　label　indicator　is　obtained　in　the　scene　of　clustering　or　semisupervised　classification.　Furthermore,　an　adaptive　weight　vector　is　learned　to　incorporate　the　diversity　and　complementarity　of　multiview　data.　Besides,　the　relationships　between　the　proposed　scheme　and　spectral　clustering,　neighborhood　embedding　and　manifold　embedding　are　analyzed　theoretically.　Finally,　comprehensive　comparative　experiments　are　conducted　with　several　state-of-the-art　multiview　clustering　and　semisupervised　classification　methods　on　eight　real-world　datasets.　The　experimental　results　demonstrate　the　superiority　of　the　proposed　method　in　terms　of　both　clustering　and　classification　performance.