A　deep　belief　network　(DBN)　is　effective　to　create　a　powerful　generative　model　by　using　training　data.　However,　it　is　difficult　to　fast　determine　its　optimal　structure　given　specific　applications.　In　this　paper,　a　growing　DBN　with　transfer　learning　(TL-GDBN)　is　proposed　to　automatically　decide　its　structure　size,　which　can　accelerate　its　learning　process　and　improve　model　accuracy.　First,　a　basic　DBN　structure　with　single　hidden　layer　is　initialized　and　then　pretrained,　and　the　learned　weight　parameters　are　frozen.　Second,　TL-GDBN　uses　TL　to　transfer　the　knowledge　from　the　learned　weight　parameters　to　newly　added　neurons　and　hidden　layers,　which　can　achieve　a　growing　structure　until　the　stopping　criterion　for　pretraining　is　satisfied.　Third,　the　weight　parameters　derived　from　pretraining　of　TL-GDBN　are　further　fine-tuned　by　using　layer-by-layer　partial　least　square　regression　from　top　to　bottom,　which　can　avoid　many　problems　of　traditional　backpropagation　algorithm-based　fine-tuning.　Moreover,　the　convergence　analysis　of　the　TL-GDBN　is　presented.　Finally,　TL-GDBN　is　tested　on　two　benchmark　data　sets　and　a　practical　wastewater　treatment　system.　The　simulation　results　show　that　it　has　better　modeling　performance,　faster　learning　speed,　and　more　robust　structure　than　existing　models.　Note　to　Practitioners-Transfer　learning　(TL)　aims　to　improve　training　effectiveness　by　transferring　knowledge　from　a　source　domain　to　target　domain.　This　paper　presents　a　growing　deep　belief　network　(DBN)　with　TL　to　improve　the　training　effectiveness　and　determine　the　optimal　model　size.　Facing　a　complex　process　and　real-world　workflow,　DBN　tends　to　require　long　time　for　its　successful　training.　The　proposed　growing　DBN　with　TL　(TL-GDBN)　accelerates　the　learning　process　by　instantaneously　transferring　the　knowledge　from　a　source　domain　to　each　new　deeper　or　wider　substructure.　The　experimental　results　show　that　the　proposed　TL-GDBN　model　has　a　great　potential　to　deal　with　complex　system,　especially　the　systems　with　high　nonlinearity.　As　a　result,　it　can　be　readily　applicable　to　some　industrial　nonlinear　systems.