Accurate　carbon　price　forecast　plays　a　vital　role　in　energy　conservation,　emission　reduction　and　environmental　protection.　In　previous　studies,　more　attention　was　focused　on　the　prediction　accuracy　and　stability,　while　the　problem　of　disharmony　between　the　prediction　model　and　the　data　pattern　is　usually　ignored.　Considering　the　matching　utility　with　deeper　understanding　of　data　and　model,　this　paper　proposes　a　novel　approach　to　forecast　carbon　price,　which　combines　the　data　preprocessing　mechanism,　decomposition　technology,　forecast　module　with　selection　and　matching　strategy　and　ensemble　model　based　on　an　original　hybrid　optimization　algorithm.　According　to　a　comprehensive　evaluation　index　in　consideration　of　several　evaluation　perspectives,　the　optimal　parameter　structures　of　the　three　forecast　models　are　selected　in　this　framework.　Then,　the　data　components　decomposed　by　variational　mode　decomposition　are　reconstructed　into　three　novel　range　entropy　series　with　different　levels　of　complexity　by　range　entropy.　As　a　result,　the　matching　relation　between　the　three　forecasting　models　and　the　three　range　entropy　series　is　correspondingly　established.　Additionally,　a　feedback　neural　network　optimized　by　hybrid　optimization　algorithm,　which　persists　more　superiorities　of　reasonable　weight　assignment　than　the　usual　ensemble　method,　is　initially　used　to　synthesize　three　forecasting　results　of　range　entropy　series.　The　carbon　price　data　from　four　different　trading　markets　in　China　is　used　to　test　the　novel　approach　and　the　experimental　results　indicate　that　it　does　enhance　the　performance　of　carbon　price　forecasting,　and　provide　a　convincing　tool　for　the　operation　and　investment　of　the　carbon　markets.　©　2021　Elsevier　Inc.