The　number　of　rules　and　parameter　values　in　extended　belief　rule　base　(EBRB)　affect　the　accuracy　and　computing　efficiency　of　the　EBRB　inference　model.　Therefore,　this　paper　proposes　an　improved　EBRB　inference　method　based　on　rule　clustering　and　parameter　learning,　called　RCPL-EBRB　model.　The　principles　of　the　proposed　model　include:　The　density　clustering　analysis　is　firstly　used　to　perform　the　rule　clustering　of　the　EBRB,　so　as　to　identify　invalid　extended　belief　rules　and　improve　the　modeling　process　of　the　traditional　EBRB.　Then,　the　rule　clusters　obtained　by　clustering,　namely　sub-EBRB,　are　used　as　basic　units　for　parameter　learning　and　rule　reasoning,　so　as　to　improve　the　accuracy　and　computing　efficiency　of　the　RCPL-EBRB　model.　Finally,　the　datasets　of　nonlinear　function　fitting　and　benchmark　classification　problems　are　introduced　to　verify　the　effectiveness　of　the　proposed　model　and　carry　out　parameters　sensitivity　analysis.　Results　show　that　the　RCPL-EBRB　model　has　higher　accuracy　than　the　existing　EBRB　inference　model　and　traditional　machine　learning　methods.　©　2024　Northeast　University.　All　rights　reserved.