To　solve　the　problem　that　the　selection　and　assignment　of　hidden　layer　parameters　of　stochastic　configuration　network　(SCN)　affects　its　prediction　accuracy,　a　SCN　parameter　optimization　method　was　proposed　based　on　the　chaotic　feedback　sooty　tern　optimization　algorithm　(CFSTOA).　First,　the　Tent　mapping,　linear　factor　adjustment　strategy　and　disadvantaged　population　feedback　principle　were　used　to　improve　the　sooty　tern　optimization　algorithm　(STOA)　to　enhance　the　local　search　capability　and　to　obtain　a　CFSTOA　with　faster　convergence　speed　and　higher　convergence　accuracy.　Then,　the　CFSTOA　was　used　to　optimize　the　regularization　parameter　and　the　scale　factor　of　weight　biases　of　the　SCN　to　obtain　the　optimal　hidden　layer　parameters.　Finally,　the　performance　of　the　CFSTOA　was　tested　through　10　benchmark　functions　and　4　standard　regression　datasets.　Results　show　that　the　CFSTOA　has　a　faster　convergence　speed　and　is　less　likely　to　fall　into　a　local　optimum,　which　can　improve　the　prediction　accuracy　and　training　speed　of　the　SCN　algorithm.　©　2023　Beijing　University　of　Technology.　All　rights　reserved.