Recently,　multi-view　learning　has　captured　widespread　attention　in　the　machine　learning　area,　yet　it　is　still　crucial　and　challenging　to　exploit　beneficial　patterns　from　multi-view　data.　Specifically,　very　limited　work　has　been　devoted　to　multi-view　semi-supervised　learning,　where　only　a　small　number　of　labeled　data　points　are　available　for　model　training.　Therefore,　a　simple　yet　efficient　seeded　random　walk　scheme　is　proposed　in　this　paper　to　address　the　multi-view　semi-supervised　classification　problem,　where　known　labeled　data　points　serve　as　random　seeds　to　be　walked　with　certain　probability.　In　this　scheme,　the　semi-supervised　classification　indicator　is　obtained　based　primarily　on　an　arrival　probability　and　a　reward　matrix,　which　are　computed　by　leveraging　an　initial　distribution　from　some　random　seeds.　Besides,　theoretical　analyses　are　then　provided　to　indicate　a　connection　of　the　proposed　model　with　the　existing　manifold　ranking　method.　Finally,　comprehensive　experiments　on　eight　publicly　available　data　sets　demonstrate　the　superiority　of　the　proposed　model　against　compared　state-of-the-art　semi-supervised　methods　and　fully　supervised　classifiers.　Furthermore,　experimental　results　also　suggest　that　the　proposed　method　comes　with　positive　robustness　and　promising　generalization　capability　in　terms　of　data　classification.　(C)　2021　Elsevier　B.V.　All　rights　reserved.