The　wastewater　treatment　process　(WWTP)　is　a　complex　biochemical　reaction　process　that　features　highly　nonlinear,　non-Gaussian　and　time　correlation.　As　a　new　monitoring　method,　deep　recurrent　neural　net-work　(DRNN)　has　an　effective　performance　in　dealing　with　the　nonlinear　and　time　correlation　of　the　data,　but　it　is　insufficient　in　dealing　with　the　non-Gaussian　characteristics.　In　this　study,　an　effective　deep　recurrent　network　with　high-order　statistic　information　(HSI-DRN)　is　proposed　for　solving　the　insufficiency　in　dealing　with　the　non-Gaussian　characteristics.　The　proposed　method　extracts　the　high-order　statistics　characteristics　by　the　over-complete　independent　component　analysis　(OICA)　method.　After　that,　weights　in　DRNN　can　be　trained　based　on　the　obtained　high-order　statistics　information　and　their　corresponding　fault　labels.　Because　of　the　architecture　of　the　network,　the　ability　of　extracting　the　non-Gaussian　characteristics　by　HSI-DRN　can　be　improved　by　the　high-order　statistics　characteristics.　Finally,　HSI-DRN　can　generate　visual　monitoring　results　by　discretizing　the　output　data,　which　leads　to　more　intuitively　reflection　of　faults.　Simulation　studies　on　the　BSM1　model　has　been　performed　to　verify　the　performance　of　the　method.　For　the　different　faults,　the　proposed　method　have　higher　fault　monitoring　ability　with　average　false　alarm　rate　(FAR)　and　missed　detection　rate　(MAR)　for　respective　0.0215%　and　0.586%　when　compared　to　other　state-of-the-art　fault　monitoring　methods.