Based　on　production　data　for　1180　MPa　level　ultra-high　strength　cold-rolled　dual-phase　(DP)　steel　coils,　the　chemical　principal　component　extraction　method　based　on　principal　component　analysis,　the　hyper-parameter　optimization　method　combining　grid　search　and　cross-validation　were　studied,　and　GBDT　prediction　models　of　DP　steel　mechanics　properties　were　established.　The　predicted　results　were　compared　with　those　of　BP　neural　network　models　and　generalized　additive　models　(GAM).　To　improve　the　prediction　accuracy　of　elongation　at　break,　based　on　the　GBDT　prediction　model　with　high　prediction　accuracy,　a　prediction　correction　model　of　elongation　at　break　considering　error　compensation　was　established　through　the　model　prediction　error　classification　model　and　the　model　prediction　correction　method　considering　error　compensation,　and　the　prediction　accuracy　of　the　elongation　correction　model　reaches　94.63%　within　an　absolute　error　range　of　+0.9%.　The　DP　steel　property　prediction　model　performs　well　with　good　prediction　accuracy　during　online　operation,　meeting　production　requirements,　and　is　helpful　for　online　quality　monitoring　of　mechanics　properties.　©　2023　China　Mechanical　Engineering　Magazine　Office.　All　rights　reserved.