We　proposed　a　novel　model　to　predict　human＇s　visual　attention　when　free-viewing　webpages.　Compared　with　natural　images,　webpages　are　usually　full　of　salient　regions　such　as　logos,　text,　and　faces,　while　few　of　them　attract　human＇s　attention　in　a　short　sight.　Moreover,　webpages　perform　distinct　viewing　patterns　which　are　quite　different　from　the　natural　images.　In　this　paper,　we　introduced　multi-features　according　to　our　observation　on　webpages　characters　and　related　eye-tracking　data.　Further,　in　order　to　achieve　a　flexible　adaptation　to　various　types　of　webpages,　we　employed　a　machine-learning　framework　based　on　our　proposed　features.　Experimental　results　demonstrate　that　our　model　outperforms　other　state-of-the-art　methods　in　webpage　saliency　prediction.　©　2016　IEEE.