In the sintering process , accurate prediction of the sintering burn-through point is essential to control the operation of the  sintering machine , as  it determines  the  quality  of the  sintered  material and the efficiency of energy use.  HoWeVer , the current common sintering burn-through point prediction models mainly focus on single-step prediction , and they tend to ignore the spatio-temporal characteris- tics of the sintering process data.  In VieWof the  multi-step prediction task requirements of the  sintering burn-through point and the  complex  spatio-temporal  characteristics  of process  data , a  multi-step  pre- diction model of the sintering burn-through point based on encoder-decoder architecture Was proposed.  The spatio-temporal encoder-task decoder architecture  effectiVely  extracts  the  spatio-temporal  features of sintering process data by stacking temporal conVolutional netWorks and spatial attention mechanism , and adopts a task-specific guidance decoder to correspond to each step of prediction in the form of in- dependent units.  The effectiVeness of the proposed method Was Verified by an actual sintering industrial process case.  This study can assist operators to obtain information about the future state of the sintering process in adVance , so as to adjust the operating parameters more accurately , reduce the quality fluc - tuation and energy Waste caused by the lag , and improVe the quality stability of the sinter.