The　wastewater　treatment　process　(WWTP)　is　a　complex　nonlinear,　uncertain　and　dynamic　physical　and　biochemical　reaction　process.　The　non-linearity,　uncertainty　and　dynamicity　of　the　WWTP　increase　the　difficulty　of　extracting　data　features,　and　also　make　it　difficult　to　monitor　the　faults　in　this　process.　Aiming　at　the　problems　of　non-linearity,　uncertainty　and　dynamicity,　a　slow　feature　variational　autoencoder　(SFAVAE)　process　monitoring　model　is　proposed.　With　the　dynamicity　of　WWTP　data　taken　into　account,　the　slow　feature　analysis　algorithm　(SFA)　is　used　to　extract　the　slowly　changing　dynamic　features　of　wastewater　data.　The　variational　autoencoder　can　impose　Gaussian　distribution　restrictions　on　its　hidden　layer　features,　so　that　it　can　simultaneously　learn　nonlinear　and　certain　features　that　obey　the　Gaussian　distribution　to　deal　with　the　nonlinearity　and　uncertainty　of　data.　Finally,　the　hidden　layer　space　of　the　variational　autoencoder　model　is　used　to　construct　hidden　layer　feature　statistics　Z2　to　realize　process　monitoring.　Compared　with　the　principal　component　analysis　(PCA),　independent　component　analysis　(ICA),　kernel　principal　analysis　(KPCA)　and　variational　auto-encoder　(VAE)　models,　the　experimental　results　of　the　benchmark　simulation　model　1　(BSM1)　model　show　that　the　SFAVAE　model　has　higher　effectiveness　in　process　monitoring.　©　2021　IEEE.