Aiming at the problems of prior knowledge and slow algorithm convergence for feature selection in the quantitative analysis of laser-induced breakdown spectroscopy (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 L1 regular term.This method first selects the feature that has the greatest correlation with the target element,then compresses the feature dimension to within 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 0.995 and 0.991,root mean square error (RMSE) were 4.634 mg/kg and 6.078 mg/kg,mean absolute error (MAE) were 6.100% and 6.441%.The number of features is reduced from 42 870 of the original spectral data to 5,which takes only 0.97 s.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 analysis models,providing an efficient method for feature selection in quantitative analysis of LIBS technology.