The　Belief　Rule-Based　(BRB)　system　faces　the　rule　combination　explosion　issue,　making　it　challenging　to　construct　the　rule　base　efficiently.　The　Extended　Belief　Rule-Based　(EBRB)　system　offers　a　solution　to　this　problem　by　using　data-driven　methods.　However,　using　EBRB　system　requires　the　traversal　of　the　entire　rule　base,　which　can　be　time-consuming　and　result　in　the　activation　of　many　irrelevant　rules,　leading　to　an　incorrect　decision.　Existing　search　optimization　methods　can　somewhat　solve　this　issue,　but　they　have　limitations.　Moreover,　the　calculation　of　the　rule　activation　weight　only　considers　the　similarity　between　input　data　and　a　single　rule,　ignoring　the　influence　of　the　rule　linkage.　To　address　these　problems,　we　propose　a　new　EBRB　system　based　on　the　K-Nearest　Neighbor　graph　index　(Graph-EBRB).　We　introduce　the　Hierarchical　Navigable　Small　World　(HNSW)　algorithm　to　create　the　K-Nearest　Neighbor　graph　index　of　the　EBRB　system.　This　index　allows　us　to　efficiently　search　and　activate　a　set　of　key　rules.　We　also　propose　a　new　activation　weight　calculation　method　based　on　the　Graph　Convolution　Neural　Network　(GCN),　and　we　optimize　the　system　performance　using　a　parameter　learning　strategy.　We　conduct　a　comprehensive　experiment　on　14　commonly　used　public　data　sets,　and　the　results　show　that　Graph-EBRB　system　significantly　improves　the　reasoning　efficiency　and　accuracy　of　the　EBRB　system.　Finally,　we　apply　the　Graph-EBRB　system　to　tree　disease　identification　and　achieve　excellent　classification　performance,　identifying　over　90%　of　the　diseased　trees　on　the　complete　dataset.　©　2024　Elsevier　Inc.