Stochastic　configuration　networks　(SCNs)　have　been　extensively　employed　in　the　modeling　of　nonlinear　system　regression　tasks.　The　SCN　model　stops　training　by　setting　the　maximum　number　of　nodes　in　the　hidden　layer　or　the　expected　error　tolerance.　If　the　parameters　are　not　set　properly,　it　is　easy　to　cause　the　model　to　overfit.　To　address　this　problem,　a　pruning　SCN　algorithm　based　on　node　similarity　and　contribution　(SCPSCN)　is　proposed.　First　of　all,　we　introduce　the　definitions　of　node　similarity.　Then,　basing　on　Garson　algorithm,　a　method　that　can　effectively　judge　the　contribution　of　hidden　layer　nodes　is　introduced.　Finally,　the　redundant　and　low-quality　nodes　are　deleted　based　on　the　similarity　and　contribution　so　that　the　optimal　network　structure　of　SCN　is　obtained.　The　proposed　method　has　been　successfully　applied　to　prediction　of　BOD　in　a　wastewater　treatment　plant.　The　experimental　results　show　that　SCPSCN　has　better　prediction　performance　than　the　original　SCN　method.　©　2024　IEEE.