Link　prediction　of　complex　network　intends　to　estimate　the　probability　of　existence　of　links　between　two　nodes.　In　order　to　improve　link　prediction　accuracy　and　fully　exploit　the　potentialities　of　nodes,　many　studies　focus　more　on　the　influence　of　degree　on　nodes　but　less　on　the　hybrid　influence　of　degree　and　H-index.　The　nodes　with　a　larger　degree　have　more　neighbors,　and　the　nodes　with　larger　H-index　have　more　neighbors　of　neighbors.　Meanwhile,　weak　ties　consisting　of　neighbors　with　a　small　degree　have　powerful　strength　of　intermediary　ability　and　a　high　probability　of　passing　similarity.　A　novel　link　prediction　model　is　proposed　considering　the　hybrid　influence　of　degree　and　H-index　and　weak　ties,　which　is　called　Hybrid　Weak　Influence,　marked　as　HWI.　After　experimenting　with　nine　real　datasets,　the　results　show　that　this　method　can　significantly　improve　the　link　prediction　accuracy,　compared　with　the　empirical　methods:　Common　Neighbors　(CN),　Resource-Allocation　(RA)　and　Adamic/Adar　(AA).　Meanwhile,　the　computation　complexity　is　less　than　the　long　path　algorithm　of　LP,　SRW,　PCEN.