An　extended　belief　rule-based　(EBRB)　system　is　superior　to　existing　rule-based　systems　in　managing　several　types　of　uncertain　information　and　modeling　complex　issues　effectively　and　efficiently.　However,　the　accuracy　and　interpretability　of　the　EBRB　system　still　need　to　be　enhanced　by　addressing　the　following　shortcomings:　the　interpretability　of　the　intermediate　variables　in　the　EBRB　system　should　be　definite　and　the　system　parameters　must　be　effectively　determined.　Therefore,　we　distinguish　discrete　and　continuous　data　types　to　perform　sensitivity　analysis　twice:　first,　on　the　rule　inference　scheme　to　study　the　interpretability　of　individual　matching　degrees　and　activation　weights;　and　second,　on　the　rule　generation　scheme　to　examine　the　effect　of　utility　values　and　attribute　weights　on　the　accuracy　of　the　EBRB　system.　Based　on　the　analyses,　we　propose　a　novel　activation　weight　calculation　method　and　parameter　optimization　method　to　enhance　the　interpretability　and　accuracy　of　the　EBRB　system,　respectively.　We　then　present　three　case　studies　to　elucidate　the　effectiveness　of　the　proposed　methods.　The　results　indicate　that　the　enhanced　EBRB　system　prevents　counterintuitive　and　insensitive　situations　and　obtains　better　accuracies　than　some　studies.