The　widely　applied　belief-rule-based(BRB)　system　has　demonstrated　its　advantages　in　handling　both　qualitative　and　quantitative　information.　As　an　extension　of　BRB　system,　the　extended　belief-rule-based(EBRB)　system　bridges　the　rule-based　methods　and　data-driven　methods　by　efficiently　transforming　data　into　extended　belief　rules(EBRs).　Many　works　have　been　done　to　apply　EBRB　system　in　addressing　classification　problems.　However,　the　problems　of　making　use　of　all　attributes　indiscriminately　and　activating　almost　all　EBRs　still　affect　the　accuracy　and　computational　efficiency　of　EBRB　system.　In　this　paper,　a　structure　optimization　method　for　EBRB(SO-EBRB)　system,　including　attribute　optimization　and　rule　activation,　is　proposed　to　address　aforementioned　problems.　In　the　attribute　optimization,　a　weighted　minimum　redundancy　maximum　relevance(MRMR)　method　is　proposed,　where　the　relevance　between　attributes　and　label　as　well　as　the　redundancy　among　attributes　are　used　to　evaluate　attributes.　Afterwards,　the　proposed　attribute　weight　calculation　method　is　utilized　to　assign　attribute　weights　for　the　EBRB　system.　In　rule　activation,　an　improved　minimum　centre　distance　rule　activation(MCDRA)　method,　which　considering　the　weights　of　attributes　in　distance　calculation,　is　used　to　activate　customized　EBRs　for　input　query　data.　15　benchmark　classification　data　sets　are　utilized　to　verify　the　effectiveness　of　the　proposed　SO-EBRB　method.　The　results　show　that,　compared　with　conventional　EBRB　system,　the　SO-EBRB　system　achieves　higher　classification　accuracy,　lower　rule　activation　ratio　and　less　response　time.　Additionally,　comparison　between　the　proposed　method　and　some　state-of-art　machine　learning　algorithms　demonstrates　that　the　SO-EBRB　system　achieves　prominent　performance　in　addressing　classification　problems.　©　2020　Elsevier　B.V.