Predicting　burn-through　point　(BTP)　in　advance　is　a　quite　critical　task　for　the　sintering　process.　However,　sintering　is　a　complex　physicochemical　reaction　process,　and　the　strong　spatial-temporal　correlations　of　data　make　the　multi-step　prediction　task　very　challenging.　The　previous　BTP　multi-step　prediction　model　only　extracts　spatial　features　in　the　high-level　layers,　leaving　the　spatial　features　in　the　low-level　layers　not　learned.　Specifically,　the　previous　model　only　considers　the　relationships　between　the　process　variables　and　BTP,　ignoring　the　spatial　coupling　relations　among　process　variables.　Further,　the　existing　loss　function　is　mainly　based　on　Euclidean　distance,　which　cannot　learn　dynamic　information　of　multi-step　prediction　sequence.　To　tackle　these　problems,　in　this　study,　we　propose　a　3D　convolution-based　BTP　multi-step　prediction　model　(CBMP)　to　simultaneously　capture　spatial-temporal　features.　First,　the　3D　convolution　is　employed　to　capture　the　spatial-temporal　features　from　low-level　to　high-level　layers　at　the　same　time.　Secondly,　a　spatial-temporal　recalibration　block　is　proposed　to　further　refine　the　extracted　features　to　increase　the　contributions　of　informative　features　and　suppress　the　less　useful　ones.　Finally,　we　design　a　time-aware　multi-step　prediction　loss　function　to　dynamically　weigh　the　similarity　between　the　actual　sequence　and　the　predicted　sequence.　The　experimental　results　on　two　real-world　BTP　datasets　demonstrate　the　effectiveness　and　feasibility　of　the　proposed　model　on　the　BTP　multi-step　prediction　task.　IEEE