Big　data　classification　problems　have　drawn　great　attention　from　diverse　fields,　and　many　classifiers　have　been　developed.　Among　those　classifiers,　the　extended　belief　rule-based　system　(EBRBS)　has　shown　its　potential　in　both　big　data　and　multiclass　situations,　while　the　time　complexity　and　computing　efficiency　are　two　challenging　issues　to　be　handled　in　EBRBS.　As　such,　three　improvements　of　EBRBS　are　proposed　first　in　this　paper　to　decrease　the　time　complexity　and　computing　efficiency　of　EBRBS　for　multiclass　classification　under　the　assumption　of　large　amount　of　data,　including　the　strategy　to　skip　rule　weight　calculation,　a　simplified　evidential　reasoning　algorithm,　and　the　domain　division-based　rule　reduction　method.　This　turns　out　to　be　a　micro　version　of　the　EBRBS,　called　Micro-EBRBS.　Moreover,　one　of　commonly　used　cluster　computing,　named　Apache　Spark,　is　then　applied　to　implement　the　parallel　rule　generation　and　inference　schemes　of　the　Micro-EBRBS　for　big　data　multiclass　classification　problems.　The　comparative　analyses　of　experimental　studies　demonstrate　that　the　Micro-EBRBS　not　only　can　obtain　a　desired　accuracy　but　also　has　the　comparatively　better　time　complexity　and　computing　efficiency　than　some　popular　classifiers,　especially　for　multiclass　classification　problems.