In　the　rapidly　evolving　landscape　of　industrial　and　societal　progress,　technology　convergence　plays　a　pivotal　role.　This　dynamic　process　is　usually　characterized　by　the　emergence　of　new　nodes　and　new　links.　With　the　long-term　and　recent　interests　in　predicting　technology　convergence,　link　prediction　has　become　the　primary　approach　on　the　basis　of　large-scale　patent　data.　Though,　the　problem　of　node　dynamics　is　still　not　addressed　in　the　literature.　For　this　purpose,　this　paper　presents　a　technology　convergence　prediction　framework　with　three　core　modules　as　follows.　(1)　A　candidate　node　set　is　introduced　during　the　network　construction　phase,　mimicking　the　generation　of　newly-emerging　nodes.　(2)　An　inductive　graph　representation　learning　approach　is　deployed　to　generate　feature　vectors　for　newly-emerging　nodes　as　well　as　existing　ones.　(3)　The　evaluation　criteria　are　revised　to　shift　from　the　predictable　range　to　the　actual　predicted　range,　which　can　provide　a　more　realistic　assessment　of　predictive　performance.　Finally,　experimental　results　on　the　domain　of　cancer　drug　development　validate　the　feasibility　and　effectiveness　of　our　framework　in　capturing　the　dynamics　of　technology　convergence,　especially　concerning　the　relationships　of　newly　emerged　nodes　and　links.　This　study　provides　valuable　insights　into　technology　convergence　dynamics　and　points　to　future　research　and　applications.