For　predicting　the　value　of　the　quality　variable　in　fermentation　processes,　traditional　data-driven　methods　do　not　use　information　in　large　amounts　of　unlabelled　data.　To　solve　this　data-rich　but　information-poor　(DRIP)　problem,　a　teacher　student　stacked　sparse　recurrent　autoencoder　(TS-SSRAE)　model　is　proposed.　Compared　with　traditional　data-driven　methods,　the　proposed　method　has　three　main　advantages.　First,　an　autoencoder　is　an　unsupervised　method　which　can　effectively　extract　rich　information　in　unlabelled　data.　The　proposed　stacked　recurrent　autoencoder　(SRAE)　with　long　short-term　memory　(LSTM)　recurrent　neural　unit　is　superior　to　traditional　autoencoders　when　extracting　the　dynamic　correlation　information　in　the　fermentation　process.　Second,　sparse　constraints　can　make　it　much　easier　for　hidden　neurons　to　obtain　useful　information　in　a　single　moment.　Finally,　the　LSTM　recurrent　neural　unit　is　complex　and　the　inputs　of　a　SRAE　must　be　a　sequence,　which　increases　the　complexity　of　the　model　to　a　certain　extent.　So,　the　knowledge　distillation　is　employed　to　simplify　the　model　and　reduce　the　computing　time.　In　order　to　demonstrate　its　effectiveness,　the　proposed　method　is　applied　to　the　penicillin　fermentation　process　for　a　simulation　experiment　and　Escherichia　coli　production　of　interleukin-2.　The　results　show　that　the　proposed　method　based　on　TS-SSRAE　can　have　better　performance　than　conventional　methods.