In recent years, deep learning has been widely used in the field of Raman spectral classification. However, the majority of the training and test sets are generated by the same device (generally a portable Raman spectrometer), with little difference between them, and the trained model may not be directly applicable to other devices. In this study, we established a database of six cephalosporin Raman spectra and proposed a classification algorithm VGGNeXt for cephalosporin Raman spectra. VGGNeXt takes inspiration from ConvNeXt, borrows some tricks from Swin-T, and re-improves VGG. Training data were high-resolution spectra from a benchtop Raman spectrometer, and test data were low-resolution spectra from a portable Raman spectrometer. The impact of preprocessing and dataset size on algorithm accuracy was explored. The results show that our network outperforms other comparative algorithms in all cases. After preprocessing, the VGGNeXt model achieves 100% accuracy on both full and halved data sets, and 99.9% accuracy when there are only 10 data for each cephalosporin class. The results show that the experimental ideas and processing methods in this paper solve the problems of model transfer and instrument standardization to a certain extent, and the model has good robustness.