Propose an inspection method for food packaging that integrates differential Raman spectroscopy, feature extraction using the VGG16 model, and cluster analysis, as well as a method for expanding differential Raman spectra with DCGAN. The aim is to address the issues of extensive feature extraction work and limited sample quantities in traditional inspection methods.Researchers initially collected spectral data from 66 different food packaging samples, resulting in a total of 71 spectral data samples. These data were plotted into spectral graphs using python and manually classified (Fig.1) with prior knowledge (Tab.1). Subsequently, the VGG16 model was used to extract 512-dimensional feature vectors from the spectral graphs. To verify the effectiveness of VGG16 feature extraction, Principal Component Analysis (PCA) and Sammon mapping were employed for dimensionality reduction (Fig.2), followed by K-means and Gaussian Mixture Model (GMM) clustering analysis on the reduced data (Fig.4), comparing the clustering results with manual classification outcomes. Additionally, researchers explored the quality of spectral graphs generated by DCGAN under various training strategies (Fig.6, Fig.9) and compared the quality differences between generated and actual spectral graphs using the VGG16-PCA visualization method (Fig.7, Fig.8, Fig.10, Fig.11). The VGG16-PCA-K-means and VGG16-PCA-GMM clustering methods achieved accuracy rates of 91.5% and 88.7%, respectively, confirming the high efficiency and accuracy of VGG16 in extracting differential Raman spectral features. When the proportion of generated spectral graphs increased, visualization results showed significant changes in the distribution of actual spectral graphs, and the distances between generated graphs and their similar counterparts also increased (Fig.10). Moreover, as the number of DCGAN iterations increased, the distance between generated and actual spectral graphs in the visualization diagram became closer, indicating higher quality of the generated graphs (Fig.11).This study successfully integrated VGG16 and DCGAN into the differential Raman spectroscopy inspection of food packaging. The VGG16 model effectively extracted spectral graph features and achieved high classification accuracy rates through PCA dimensionality reduction and K-means and GMM clustering algorithms. The training strategy of DCGAN and the quality of generated spectral graphs significantly impacted the results of VGG16 feature extraction; by adjusting the training strategy, higher quality spectral graphs can be generated, thereby enhancing the accuracy of feature extraction clustering. Although this study did not establish a classification model, the work of generating spectral graphs laid the foundation for building more accurate and efficient classification models in the future. Future work will focus on exploring strategies to improve the quality of generated samples to further enhance the accuracy and efficiency of the inspection.