Plastic is a commonly used polymer in daily life. With the increasing amount of waste plastic, the resulting environmental pollution has become more severe, making the classification and recycling of waste plastic an urgent issue. Different types of plastics require different recycling methods, so researching plastic classification methods is of great significance. Laser-Induced Breakdown Spectroscopy (LIBS) is an elemental analysis technique based on atomic emission spectroscopy, offering advantages such as rapid analysis, no sample pretreatment required, and in-situ analysis, which provides convenience for plastic classification. Raman Spectroscopy (RS) is a molecular structure characterization technique based on Raman scattering theory, which offers advantages such as simultaneous multi-element analysis, low sample quantity requirements, and minimal sample damage, also facilitating plastic classification. This paper will utilize LIBS and RS technologies to collect spectral information from both atomic and molecular perspectives of plastics, and then merge the two types of spectral information to obtain a fused spectrum. By using LIBS spectra, RS spectra, and fused spectra in conjunction with the Random Forest machine learning algorithm (RF) to build models for plastic classification and identification, a comparison of the classification accuracy of the three models reveals that the fused spectrum can improve classification accuracy. During the model-building process, with the same number of test sets, the number of training sets affects both the model construction time and classification accuracy. Experiments were conducted on the accuracy and model construction time for different ratios of test sets to training sets, concluding that a ratio of 1∶3 is the most suitable, achieving an accuracy of 96%. In addition to the impact of the training set, the preprocessing methods of spectral data also affect the classification accuracy of the plastic fusion spectrum. The experiment-employed a sparsity-based baseline estimation denoising method to process the fusion spectral data and rebuild the model, thereby increasing the classification accuracy of plastics to 100%. The experimental results indicate that when the ratio of the test set to the training set is 1∶3, the fused spectral data has a significant advantage in classification accuracy compared to single spectral data. The classification accuracy of the preprocessed fused spectral data can be improved to 100%.