The　micro-extended　belief　rule-based　system　(Micro-EBRBS)　is　an　advanced　rule-based　system　and　has　shown　its　superior　ability　in　solving　big　data　problems.　To　overcome　the　activation　rule　incompleteness　and　inconsistency　of　Micro-EBRBS,　a　new　concept,　named　activation　factor　(AF),　is　introduced　to　revise　the　calculation　of　individual　matching　degree　and,　furthermore,　an　AF-based　inference　(AFI)　method　is　proposed　for　improving　Micro-EBRBS.　A　comparative　analysis　study　is　conducted　using　three　classification　datasets.　Results　demonstrate　that　the　proposed　AFI　method　can　not　only　improve　the　accuracy　of　Micro-EBRBS,　but　also　reduce　the　number　of　failed　data　in　the　process　of　rule　inference.