Link　prediction　based　on　traditional　models　have　attracted　many　interests　recently.　Among　all　models,　the　ones　based　on　topological　similarity　have　achieved　great　success.　However,　researchers　pay　more　attention　to　links,　but　less　to　endpoint　influence.　After　profound　investigation,　we　find　that　the　synthesis　of　degree　and　H-index　plays　an　important　role　in　modeling　endpoint　influence.　So,　in　this　paper,　we　propose　link　prediction　models　based　on　weighted　synthetical　influence,　exploring　the　role　of　H-index　and　degree　in　endpoint　influence　measurement.　Experiments　on　12　real-world　networks　show　that　the　proposed　models　can　provide　higher　accuracy.