Extended　belief　rule-based　system　(EBRBS)　as　an　integrated　data　and　knowledge　driven　rule-based　system　has　attracted　much　attention　in　the　last　few　years　and　has　been　widely　used　in　classification　problems.　Data　inconsistency　and　data　incompleteness　are　two　common　issues　and　result　in　the　decrease　of　the　accuracy　of　data-driven　model　including　EBRBS.　Although　a　dynamic　rule　activation　(DRA)　method　was　proposed　to　solve　these　two　issues　by　selecting　the　most　consistent　rules　and　has　shown　its　capability　in　improving　the　performance　of　EBRBS,　there　still　exists　some　drawbacks　in　its　efficiency　and　rationality.　Hence,　a　new　DRA　method　based　on　activation　factor　(called　AFDRA)　is　proposed　for　EBRBS　to　better　handle　the　data　inconsistency　and　data　incompleteness　issues.　Case　studies　show　that　the　AFDRA　method　not　only　has　a　great　improvement　in　the　term　of　efficiency　over　the　DRA　method,　but　also　achieves　better　performance　for　EBRBS　in　classification　problems.　©　2021　IEEE.