In　order　to　consider　the　impact　of　dynamic　features　of　the　fermentation　process　on　stage　division　and　improve　the　prediction　accuracy,　a　quality　prediction　method　based　on　attention　long　short-term　memory　(LSTM)　is　proposed.　Firstly,　the　original　3D　data　are　unfolded　along　the　batch　direction.　Partial　least　square　(PLS)　analysis　is　performed　on　each　time　slice　matrix　to　obtain　the　score　matrix　of　process　variables　and　quality　variables.　The　joint　score　matrices　are　clustered　using　the　affinity　propagation　(AP)　algorithm.　Then　the　encoder-decoder　model　is　used　to　extract　the　dynamic　characteristics　of　the　process　dynamics,　and　the　AP　algorithm　is　used　for　the　second　division.　Finally,　the　production　process　is　divided　into　different　stable　phases　and　transition　phases　through　the　comprehensive　analysis　of　the　two-step　division　results.　The　LSTM　integrated　quality　prediction　model　is　established　in　each　stage　after　the　division.　Penicillin　fermentation　simulation　data　and　E.　coli　production　data　are　tested,　and　the　results　demonstrate　the　feasibility　and　effectiveness　of　the　proposed　method.　Copyright　©2022　Control　and　Decision.