Multi-view　semi-supervised　classification　is　a　typical　task　to　classify　data　using　a　small　amount　of　supervised　information,　which　has　attracted　a　lot　of　attention　from　researchers　in　recent　years.　In　practice,　existing　methods　tend　to　focus　on　extracting　spatial　or　spectral　features　using　graph　neural　networks　without　considering　the　diversity　and　variability　of　graph　structures　and　the　contributions　of　different　views.　To　address　this　challenge,　a　framework　termed　graph　attention　fusion　network　is　proposed,　which　consists　of　two　phases:　view-specific　feature　embedding　and　graph　embedding　fusion.　In　the　former　feature　extraction　stage,　the　view-specific　feature　embedding　module　can　flexibly　focus　on　the　neighborhood　calculation　operation　to　learn　a　weight　for　each　neighboring　node.　In　the　latter　feature　fusion　stage,　the　graph　embedding　fusion　module　is　performed　by　complementarity　and　consistency　to　fuse　these　embeddings　for　semi-supervised　classification　tasks.　We　carry　out　comprehensive　experiments　in　semi-supervised　classification　on　real-world　datasets　to　substantiate　the　effectiveness　of　the　proposed　approach　compared　to　several　existing　state-of-the-art　methods.