[Objective]　This　paper　explores　ways　to　identify　lead　users　in　different　fields　of　the　open　innovation　community,　aiming　to　help　enterprises　obtain　external　knowledge　resources.　[Methods]　First,　we　used　the　LDA　to　extract　user　topics　and　construct　a　user　knowledge　bipartite　network.　Then,　we　combined　the　characteristics　of　the　lead　users＇knowledge　structure　and　traditional　individual　attributes.　Third,　we　proposed　a　link　prediction　method　based　on　the　Exponential　Random　Graph　Model　to　identify　lead　users　in　different　fields.　Finally,　we　conducted　an　empirical　study　using　the　Joint　Definition　Community　as　an　example.　[Results]　We　identified　20　lead　users　and　found　their　average　link　probability　was　greater　than　0.900.　Compared　with　traditional　link　prediction　methods,　our　method　had　the　largest　AUC　of　0.996　7,　and　the　smallest　ARC　of　0.013　2.　[Limitations]　Our　model　did　not　include　the　impacts　of　time　factors　on　user　knowledge.　[Conclusions]　This　research　enriches　the　perspectives　and　methods　of　lead　user　identification　and　lays　a　solid　foundation　for　the　follow-up　studies.　©　2023　Chin　J　Gen　Pract.　All　rights　reserved.