Being　＇too　interconnected　to　fail＇　has　made　the　risk　correlation　of　financial　institutions　and　its　influencing　factors　a　crucial　issue　in　maintaining　financial　stability.　Drawing　inspiration　from　gene　regulatory　research　using　random　forests,　this　paper　proposes　a　method　to　construct　a　network　that　captures　the　relations　between　different　indicators,　for　the　purpose　of　exploring　the　influences　between　risk　correlation　and　its　related　factors.　It　is　achieved　by　integrating　forest　fusion　and　random　permutation.　The　proposed　method　overcomes　the　limitations　of　traditional　regression　analysis,　Granger　causality　test,　and　Bayesian　networks,　while　the　introduction　of　random　permutation　enhances　the　model’s　capability　to　handle　variable　heterogeneity.　Empirical　results　based　on　46　listed　financial　institutions　in　China　from　2012　to　2022　demonstrate　that　the　constructed　network　can　identify　the　direct　or　indirect　impact　of　different　factors　on　risk　correlation　and　reveal　the　influence　paths　of　factors.　This　provides　more　comprehensive　empirical　evidence　of　complex　relationships,　highlighting　the　applicability　of　the　proposed　approach　in　addressing　this　issue　and　potentially　offering　a　useful　tool　for　financial　regulation　and　risk　management.　©　2024　Systems　Engineering　Society　of　China.　All　rights　reserved.