It　is　difficult　to　accurately　describe　the　dynamic　characteristics　of　multi-stage　time-varying　batch　processes　with　global　models,　while　every　working　point　of　traditional　local　models　needs　to　re-select　samples　that　results　in　high　requirement　of　computation.　A　local　modeling　strategy　based　on　substep　time-space　just-in-time　learning　(JITL)　was　proposed.　The　data　in　the　sample　set　of　historical　data　were　preliminarily　classified　by　an　affinity　propagation　(AP)　clustering　method.　When　the　current　input　sample　data　arrived,　the　category　of　these　data　was　determined.　The　JITL　strategy　combining　time　and　space　was　used　to　determine　local　similar　samples　in　the　subsample　of　the　category.　A　multi　kernel　partial　least　squares　(MKPLS)　monitoring　model　was　established　using　local　similar　samples.　The　effectiveness　of　the　proposed　method　was　validated　via　simulation　of　penicillin　fermentation　and　E.　coli　production　of　interleukin-2.　The　results　demonstrate　that　the　proposed　method　reduces　unnecessary　calculation　and　can　detect　faults　more　accurately　and　instantaneously.　©　2021,　Editorial　Board　of　＇Journal　of　Chemical　Engineering　of　Chinese　Universities＇.　All　right　reserved.