The　wastewater　treatment　process　(WWTP)　is　a　complex　biochemical　reaction　process　in　which　sensor　data　has　strong　nonlinear,　non-Gaussian　and　time　correlation　characteristics.　The　traditional　methods　ignore　to　consider　the　aforementioned　three　characteristics　simultaneously,　which　may　have　insufficient　feature　extraction　of　WWTP.　In　this　work,　an　Over-Complete　Deep　Recurrent　Neural　Network　(ODRNN)　method　is　proposed　to　solve　the　above　issues.　The　ODRNN　combines　the　over-complete　independent　component　analysis　(OICA)　and　binary　particle　swarm　optimization　(BPSO)　to　efficiently　extract　the　non-Gaussian　information,　and　then　the　extracted　information　is　fed　into　DRNN　to　obtain　the　time　correlation　characteristics.　In　this　way,　the　method　can　not　only　capture　the　non-linear　and　non-Gaussian　information　but　also　extract　temporal　correlation　of　WWTP　data.　Simulation　results　on　BSM1　showed　that　the　ODRNN　based　soft　sensor　method　has　higher　accuracy　and　robustness　than　other　state-of-the-art　methods.　©　2021　Elsevier　B.V.