To help the reconnaissance organs obtain more clues during the case investigation process, the material evidence of plastic and steel windows commonly used in the work was identified using efficient and data-based nondestructive identification. To analyze and identify the material evidence, principal component analysis (PCA) preprocessing was performed in conjunction with Fisher discriminant analysis (FDA)-optimal parameter combination support vector machine (SVM). The two-dimensional characterization and recognition of"brand-batch"were accomplished based on the theoretical and experimental analyses of 126 sets of Fourier transform infrared spectrum data extracted from 6 brands, such as"Jinpeng"and"Conch".Based on the PCA results of three spectral segments, namely, the complete spectrum, functional group, and fingerprint segments, a data classification model based on Fisher discriminant analysis was created. The classification accuracy of the entire spectrum segment was determined to be the highest at 66.7%. The SVM classification model was built using the eigenvalues of the entire spectrum. The effects of the penalty factor C and radial basis function (RBF) gamma value σ on the classification accuracy of the SVM classification model were investigated, and the SVM classification model based on the optimal parameter combination (C = 10, σ = 2.5) was obtained. The best classification model was used to distinguish the different batches of"Conch"brand plastic steel window samples, and the classification accuracy reached 100%. The classification results of this method are ideal as they can fulfill the needs of the rapid classification of plastic steel window cases and are expected to provide some reference for its application in the field of forensic science research.