Laser-induced breakdown spectroscopy (LIBS) plays an increasingly important role in the classification and recycling of aluminum alloys owing to its outstanding elemental analysis performance. For LIBS measurements with sample surface fluctuations, consistently and exactly maintaining the laser and fiber focus points on the sample surface is difficult, and fluctuations in the focus severely affect the stability of the spectrum. In this study, a data transfer method is introduced to reduce the effect of spectral fluctuations on the model performance. During the experiment, a focal point is placed on the sample surface. Then, keeping experimental conditions unchanged, the three-dimensional platform is only moved up and down along the z-axis by 0.5 mm, 1 mm, 1.5 mm, 2 mm and 2.5 mm, respectively. Eleven spectral datasets at different heights are collected for analysis. The KNN model is used as the base classifier, and the accuracies of the 11 datasets, from the lowest to the highest, are 11.48%, 19.71%, 30.57%, 45.71%, 53.57%, 88.28%, 52.57%, 21.42%, 14.42%, 14.42%, and 14.42%. To improve predictive performance, the difference in data distribution between the spectra collected at the sample surface and those collected at other heights is reduced by data transfer. Feature selection is introduced and combined with data transfer, and the final accuracies are 78.14%, 82.28%, 80.14%, 89.71%, 91.85%, 98.42%, 94.28%, 92.42%, 82.14%, 78.57%, and 73.71%. It can be seen that the proposed method provides a new feasible and effective way for the classification of aluminum alloys in a real detection environment.