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.　©　2021　Elsevier　B.V.