Multi-view　semi-supervised　learning　has　gained　much　attention　since　a　great　number　of　unlabeled　multi-view　data　are　easy　to　obtain　while　few　labeled　data　are　available.　Accordingly,　how　to　utilize　the　relationship　between　labeled　and　unlabeled　data　is　of　significance　in　multi-view　semi-supervised　learning.　In　this　paper,　we　propose　an　auto-weighted　manifold　embedding　model　to　address　multi-view　semi-supervised　classification　problems,　where　only　a　small　percentage　of　labeled　data　points　are　used　for　model　training.　In　the　proposed　model,　data　points　close　to　each　other　in　the　feature　space　will　be　assigned　to　similar　classes　in　the　label　space　through　manifold　embedding.　Accordingly,　the　class　information　of　labeled　data　will　be　employed　in　the　prediction　process　for　unlabeled　data.　Moreover,　an　optimal　weight　for　each　view　of　multi-view　data　is　learned　automatically,　which　enables　an　encouraging　fusion　quality　for　multi-view　manifold　embedding.　To　further　speed　up　the　calculation　process　and　reduce　the　computational　complexity　of　the　proposed　model,　an　effective　accelerated　auto-weighted　manifold　embedding　scheme　is　developed.　Besides,　theoretical　analyses　are　then　provided　to　indicate　a　tight　approximation　bound　for　the　primary　manifold　embedding　method,　while　the　accelerated　method　is　designed　by　an　order　decrease　of　magnitude　of　computational　complexity.　Finally,　comprehensive　experiments　on　eight　publicly　available　data　sets　demonstrate　the　superiority　of　the　proposed　models　over　compared　state-of-the-art　semi-supervised　methods　and　fully　supervised　classifiers.　Furthermore,　the　experimental　results　are　suggestive　of　positive　robustness　and　promising　generalization　capacity　of　the　proposed　methods.　©　2021　Elsevier　Inc.