The　existing　information　diffusion　prediction　methods　model　the　cascade　sequences　and　topological　structure　independently.　And　thus　it　is　difficult　to　learn　the　interactive　expression　of　cascade　temporal　and　structural　features　in　the　embedded　space,　and　the　portrayal　of　dynamic　evolution　of　information　diffusion　is　insufficient.　Aiming　at　this　problem,　an　information　diffusion　prediction　method　based　on　cascade　spatial-temporal　feature　is　proposed.　Based　on　the　social　network　and　diffusion　paths,　the　heterogeneous　graphs　are　constructed.　The　structural　context　of　nodes　of　heterogeneous　graphs　and　social　network　is　learned　by　graph　neural　network,　while　the　cascade　temporal　feature　is　captured　by　gated　recurrent　unit.　To　make　microscopic　information　prediction,　the　cascade　spatial-temporal　feature　is　constructed　by　fusing　structure　context　and　temporal　feature.　The　experimental　results　on　Twitter　and　Memes　datasets　demonstrate　that　the　performance　of　the　proposed　method　is　improved　to　a　certain　extent.　©　2021,　Science　Press.　All　right　reserved.