With the continuous development of the economy, a large amount of waste aluminum alloy material has been generated in the industrial construction sector. The classification and recycling of waste aluminum alloy materials can enhance the utilization efficiency of waste resources and alleviate energy tension. This paper selects five types of aluminum alloys commonly used in the industrial field to investigate the influence of filament-induced breakdown spectroscopy (FIBS) spectral preprocessing wavelet transform basis functions on the classification accuracy of aluminum alloys. The orthogonal wavelet basis functions bior2.2, bior2.4, and bior2.6 are respectively used for preprocessing the FIBS spectrum of aluminum alloys, and the rapid classification identification of aluminum alloy types is achieved by combining with linear discriminant analysis (LDA), grid search optimized support vector machine (GSSVM) and back propagation neural network (BPNN). The results show that the average recognition accuracy rates of aluminum alloy types achieved by orthogonal wavelet basis functions bior2.2, bior2.4, and bior2.6 combining with LDA - GSSVM are 90%, 100%, and 76.67%, combining with LDA-BPNN are 96.67%, 100%, and 90%, respectively. Therefore, choosing appropriate orthogonal wavelet basis functions for FIBS spectral preprocessing methods and classification algorithm plays a significant role in improving the recognition accuracy of aluminum alloy types.