In this study, laser-induced breakdown spectroscopy (LIBS) combined with machine learning algorithms was employed to identify the grades of nine homogeneous, national, standard alloy-steel samples. The original LIBS spectra of the alloy steels were processed using a statistically sensitive nonlinear iterative peak-clipping (SNIP) algorithm for continuous background subtraction. Principal component analysis (PCA) was used to reduce the dimensionality of spectral data and eliminate redundant information. The first 10 principal components constitute 94.3% of the total variance. The LIBS spectral data of the nine homogeneous alloy steels were partitioned into a 7∶3 ratio to create training and testing datasets. Based on the first 10 principal components obtained from PCA, PCA-support vector machine (SVM), PCA-decision tree, PCA-K nearest neighbor (KNN), and PCA-linear discriminant analysis (LDA) models were established for alloy-steel identification. The average accuracies of the four models for the training set are 99.06%, 97.47%, 90.47%, and 100% for the SVM, decision tree, KNN, and LDA, respectively, whereas those for the testing set are 96.29%, 79.63%, 67.04%, and 100%, respectively. The PCA-LDA model achieves a 100% identification rate for homogeneous alloy-steel grades. This study provides method and reference for the rapid identification of homogeneous alloy-steel grades using laser-induced breakdown spectroscopy.