Aiming at the problems of prior knowledge and slow algorithm convergence for feature selection in the quantitative analysis of laser-induced breakdown spect roscopy (LIBS) technology,this paper proposes a quantitative analysis method for automatically selecting features with a combination of Pearson correlation coefficient -based ranking, principal component analysis and L1regular term.This method first selects the feature that has the greatest correlation with the target element,then compresses the feature dimension to wi thin the number of samples,and finally sparses the feature weight coefficient and establishes a quantitative analysis model.This method is used to screen the characteristic subsets of Co elements in the soil and establish a quantitative analysis model.The R²(coefficient of determination) of the training set and test set of the model reached 0.995and 0.991,root mean square error (RMSE) were 4.634mg/kg and 6.078mg/kg,mean absolute error (MAE) were 6.100% and 6.441%.The number of features is reduced from 42870of the original spectral data to 5,whi ch takes only 0.97s.The results show that the method proposed in this paper can reduce the dimension of feature subsets and improve the generalization and accuracy of quantitative anal ysis models, providing an efficient method for feature selection in quantitative analysis of LIBS technology.