Considered　high　nonlinearity　and　large　transient　variation,　a　PLSR-adaptive　deep　belief　network　(PLSR-ADBN)　was　proposed　for　prediction　of　total　phosphorus　(TP)　in　effluent　of　wastewater　treatment　process　(WWTP).　The　PLSR-ADBN　was　an　improved　DBN,　a　deep　learning　model.　First,　an　adaptive　learning　rate　was　introduced　into　the　unsupervised　pre-training　stage　of　DBN　so　as　to　accelerate　convergence　rate.　Secondly,　PLSR　was　used　to　replace　gradient　fine-tuning　method　in　conventional　DBN　for　improving　prediction　accuracy.　Meanwhile,　a　Lyapunov　function　was　constructed　to　prove　convergence　of　the　PLSR-ADBN　learning　process.　Finally,　the　proposed　PLSR-ADBN　was　applied　to　an　actual　TP　prediction　in　WWTP.　The　experimental　results　show　that　the　method　has　a　fast　convergence　rate　and　a　high　prediction　accuracy,　which　can　meet　the　demands　for　TP　detection　accuracy　and　WWTP　operating　efficiency.　©　All　Right　Reserved.