In　many　basic　oxygen　furnace　(BOF)　steelmaking　processes,　if　the　furnace　endpoint　carbon　can　be　monitored　in　real　time,　it　is　a　breakthrough　for　BOF　steelmaking　intelligence.　This　paper　presents　a　deep　learning　model　used　to　predict　the　endpoint　carbon　content　in　BOF　steelmaking　process.　A　convolution　long　short-term　memory　network　based　on　attention　mechanism　(CNN-LSTM-AM)　model　is　proposed　for　time-series　data　in　BOF　process　to　extract　spatial-temporal　characteristics　of　time　sequence　features　and　a　back　propagation　(BP)　model　is　proposed　for　pre-furnace　data　to　auxiliary　increase　the　accuracy　of　the　model.　The　BOF　steelmaking　data　from　an　actual　process　were　used　for　the　testing,　the　result　shown　that　84.34%　of　prediction　result　were　within　the　±0.02　range,　which　is　better　than　use　those　two　types　of　data　and　model　individually.　　©　2024　IEEE.