The　wastewater　treatment　process　(WWTP)　is　a　physical　and　biochemical　reaction　process　with　multi-stage,　non-linear　and　non-Gaussian　characteristics.　The　multi-stage　means　that　the　data　has　diversity　in　different　substages.　In　the　process　monitoring　of　the　WWTP,　Affinity　Propagation　(AP)　algorithm　is　first　used　to　divide　the　data　of　the　WWTP　into　stages　to　process　the　stage　characteristics　of　data.　Next,　the　capability　of　Variational　Auto-Encoder　(VAE)　monitoring　model　to　restrict　the　Gaussian　distribution　in　hidden　layers　is　utilized　to　address　the　coexistence　of　non-Gaussian　and　nonlinearity.　Finally,　to　illustrate　the　superiority　and　feasibility,　the　proposed　model　is　conducted　on　the　Benchmark　Simulation　Model　1　(BSM1).　The　experimental　result　illustrates　that　the　multistage　variational　autoencoder　(M-VAE)　can　extract　the　characteristics　of　data　more　comprehensively,　with　an　improvement　of　average　monitoring　accuracy　by　37.8%,　2.7%,　12.7%　and　0.52%　in　10/8　process　faults　compared　to　the　state-of-the-art　fault　monitoring　methods　Auto-encoder　(AE),　VAE,　Deep　Recurrent　Neural　Network　(DRNN),　deep　recurrent　network　with　high-order　statistic　information　(HSI-DRNN)　respectively.