Given　the　significance　of　technology　convergence　for　innovation　and　launching　new　products,　accurately　predicting　technology　convergence　is　critical　to　the　pursuit　of　technological　innovation.　Although　previous　studies　have　been　proposed　to　predict　technology　convergence,　the　framework　using　single-layer　international　patent　classification　(IPC)　networks　neglects　the　potential　connections　between　components.　This　issue　leads　to　the　prediction　that　IPC　nodes　in　separate　components　will　not　overlap.　This　study　utilizes　IPC,　patentee,　and　topic　information　to　construct　a　technology　supernetwork　model　consisting　of　＂Topic-IPC-Patentee＂　three　layers,　aiming　to　overcome　the　issue　of　IPCs　located　in　different　components　being　unable　to　connect.　Based　on　the　supernetwork,　we　adopt　a　link　prediction　method　based　on　superedge　similarity　to　predict　the　technology　convergence　and　a　case　analysis　on　the　field　of　gene　editing　is　conducted.　According　to　experimental　results,　we　observe　that　the　technology　supernetwork　model　can　effectively　predict　the　convergence　of　gene　editing　technologies,　demonstrating　robust　predictive　performance.　The　main　contribution　of　this　research　is　to　provide　a　methodology　that　accurately　predicts　technology　convergence,　which　can　help　firms　deploy　their　research　and　development　(R&D)　strategies,　policymakers　develop　insightful　policies,　and　investors　capitalize　high-value　projects.