Deep　learning　has　been　successfully　applied　into　pattern　recognition　due　to　its　deep　architecture　and　effective　unsupervised　learning,　and　deep　belief　network　(DBN)　is　a　popular　model　based　on　deep　learning　technique.　In　this　paper,　a　DBN　identification　model　based　on　partial　least　square　regression　(PLSR),　named　PLSR-DBN,　is　proposed　for　nonlinear　system　identification.　In　order　to　improve　the　identification　accuracy,　PLSR　is　introduced　into　the　supervised　fine-tuning　of　DBN　to　elimate　the　overfitting　and　local　minimum　resulted　from　gradients-based　learning,　and　contrastive　divergence　(CD)　algorithm　is　used　in　unsupervised　pre-training.　Finally,　the　proposed　PLSR-DBN　is　tested　on　a　benchmark　nonlinear　system.　The　experiment　results　show　that　the　proposed　PLSR-DBN　has　a　better　performance　on　nonlinear　system　identification　than　other　similar　methods.　©　2017　Technical　Committee　on　Control　Theory,　CAA.