The unburned carbon content of fly ash is an important index for the working efficiency of the coal-fired boiler. In this work, laser-induced breakdown spectroscopy(LIBS) was applied to realize the quantitative analysis of unburned carbon in fly ash. Based on the detection of LIBS characteristic spectrum, the common chemometrics methods include linear model, such as multiple linear regression(MLR), partial least-squares regression(PLSR) and nonlinear model, such as extreme learning machine(ELM) model, support vector machine regression(SVR) model were proposed to the prediction analysis of unburned carbon in fly ash, and the cross-validation method was used to verify the model. The results show that the prediction results from nonlinear models are better than that of linear models, among which the SVR model based on K-CV parameter optimization is helpful to improve the prediction accuracy and accuracy of the content of unburned carbon in the fly ash. Based on the three-fold cross validation method, the R² of the model is 0.99, ARD is 1.54%, 3.45% and 3.51%, and the average value of RSD is 7.53%, 2.89%, 7.18%, respectively.