Industrial　cost　is　a　comprehensive　indicator　to　reflect　industrial　behaviors,　whereas　industrial　data　with　annotation　are　rare　because　the　annotation　process　is　very　expensive.　Data　increment　transformation　is　a　feasible　solution　to　enrich　annotated　industrial　data,　but　it　bring　a　new　challenge　in　system　modeling　because　the　size　of　transformational　data　is　the　quadratical　relationship　with　that　of　collected　data,　and　even　turn　into　big　data　problem.　Hence,　a　novel　rule-based　system　proposed　for　handling　big　data　problems,　called　micro-extended　belief　rule-based　system　(Micro-EBRBS),　is　introduced　for　industrial　cost　prediction.　Firstly,　the　Micro-EBRBS　is　improved　by　(1)　the　use　of　activation　factor　to　revise　the　calculation　of　individual　matching　degrees;　(2)　the　use　of　parameter　optimization　to　determine　the　optimal　value　of　basic　parameters.　Afterwards,　on　the　basis　of　data　increment　transformation,　a　novel　industrial　cost　prediction　model,　called　data　increment-based　Micro-EBRBS　(DIME)　model,　is　developed　to　accurately　predict　industrial　costs.　In　case　study,　13　state-own　holding　industries　with　historical　data　from　1999　to　2019　in　China　are　used　to　illustrate　the　effectiveness　of　the　DIME　model.　Comparative　results　show　that　the　DIME　model　is　more　accurate　than　some　existing　models　in　industrial　cost　prediction.