To improve the qualitative accuracy of foreign protein adulteration in milk powder, a novel method named multidimensional spectral information laser-induced breakdown spectroscopy (MSI-LIBS) was proposed, which fully mined the effective information in the spectra by integrating the absolute intensity, the first derivative spectra, and the ratio spectra. Compared with traditional LIBS, the performance of the models based on MSI-LIBS was significantly improved. The accuracy of the cross-validation set of support vector machine, k-nearest neighbor, and random subspace method-linear discriminant analysis models increased from 80.98%, 75.61%, and 79.25% to 85.17%, 79.32%, and 81.18%, respectively. The accuracy of the prediction set increased from 81.50%, 76.03%, and 79.07% to 85.82%, 79.74%, and 81.28%, respectively. Furthermore, the visualization results of t-distributed stochastic neighbor embedding also showed that there was a more obvious boundary between the spectra of different samples based on MSI-LIBS. Therefore, these results fully prove the effectiveness of MSI-LIBS in improving the performance of LIBS classification.