Laser　induced　breakdown　spectroscopy　(LIBS)　is　used　to　analyze　the　prepared　fly　ash　samples,　and　support　vector　machine　regression　(SVR)　model　is　used　to　predict　the　carbon　content　of　fly　ash.　The　structure　parameters　of　radial　basis　function　(RBF)　kernel　function　and　polynomial　function　are　optimized　by　grid　search　method,　and　then　SVR　models　based　on　internal　standard　element　characteristic　spectrum,　full　spectrum,　and　main　element　characteristic　spectrum　are　established　respectively.　The　research　shows　that　SVR　model　of　RBF　and　polynomial　kernel　function　can　achieve　the　same　analysis　accuracy　under　ideal　structural　parameters,　but　RBF　can　complete　the　model　optimization　quickly　and　is　not　easy　to　underfit.　The　analysis　accuracy　of　the　SVR　model　based　on　the　characteristic　spectrum　of　internal　standard　elements　is　similar　to　that　of　the　internal　standard　method,　and　the　SVR　model　based　on　full　spectrum　shows　obvious　overfitting　phenomenon.　The　regression　coefficient　of　the　SVR　model　based　on　the　characteristic　spectrum　of　the　main　elements　is　0.986,　the　root　mean　square　error　of　correction　is　1.79%,　and　the　root　mean　square　error　of　prediction　is　2.57%,　indicating　that　the　model　can　effectively　avoid　underfitting　and　overfitting.　©　2022,　Chinese　Lasers　Press.　All　right　reserved.