Compared with the traditional solid state lasers, the fiber lasers is conducive to the miniaturization of devices and the promotion of laser induced breakdown spectroscopy (LIBS) technology. In this paper, the fiber lasers LIBS (Fiber-LIBS) technology is applied to grade identification of aluminum alloy. The data classification, normalization, support vector machine, and principal component analysis are used to classify the grades of 24 samples of 6 kinds of aluminum alloys. The results show that, compared with the simple classification algorithm based on the support vector machine classification algorithm, the data filtering, normalization, and support vector machine combined with the principal component analysis can make the average prediction accuracy rate increase from 92.34% to 99.83%, and can decrease the modeling time more than one order of magnitude. The experimental results show the feasibility of fiber lasers used in LIBS system for the metal grade recognition.