In order to improve the accuracy of quantitative analysis of aluminum alloy, a quantitative analysis model of Cu element in aluminum alloy was established by combining laser-induced breakdown spectroscopy with multivariate linear regression, median Gaussian kernel support vector machine regression and standardized partial least squares regression. Third order minimum background removal and wavelet threshold denoising were performed on the collected LIBS spectra to improve the SNR of LIBS spectra. The optimal training set and prediction set were selected from the processed data. The calibration model was established using multi variable linear regression method, medium Gaussian kernel support vector machine regression method and normalized partial least squares fitting regression method. Two characteristic lines of Cu Ⅰ 324.80 nm and Cu Ⅰ 327.43 nm and Libs spectral data in the range of 323~329 nm were used for quantitative analysis. The fitting coefficient (R²), root mean square error (RMSEC), root mean square error of prediction (RMSEP) and average relative error (ARE) of the three Libs quantitative analysis models were compared and analyzed. The results show that compared with the multivariable linear regression method and medium Gaussian kernel support vector machine regression method, the precision and accuracy of the standardized PLSR model are significantly improved for the quantitative analysis of Cu element in aluminum alloy, and the R², RMSEC, RMSEP and ARE of the Libs calibration curves are 0.997, 0.014 Wt%, 0.129 Wt% and 3.053%, respectively. The results show that the standardized PLSR method has more advantages in improving the accuracy and generalization of the calibration model, and can effectively reduce the influence of parameter fluctuation and self-absorption effect on the quantitative analysis of aluminum alloy.