In this paper, a Wasserstein Generative Adversarial Network (WGANGP) method combining Raman spectroscopy with diffusion model improvement is proposed for improving the detection accuracy of the aging state of transformer oil-paper insulation. Raman spectroscopy, with its advantages of no contact and no loss, is used to assess the aging degree of a transformer by analysing the aging products of the oil-paper insulation material inside an oil-immersed power transformer. Combining deep learning classification models simplifies the Raman spectroscopy data preprocessing process, but such models have high requirements on the quantity and quality of training data. The long cycle of thermally accelerated aging experiments results in a relatively scarce set of valid Raman spectral data available for training, limiting the performance of the classification model. To address this challenge, a new data augmentation method, Diffusion-Based WGANGP, is introduced in this study. By combining the forward noise addition process of the denoising diffusion probabilistic model with WGANGP, the method introduces instantiated noise into WGANGP, removes the complex up-sampling process in the generator structure of the traditional WGANGP, simplifies the data augmentation model structure, and facilitates the optimization of model parameters. Compared with the traditional GAN and its variants, this method not only maintains the baseline drift trend of the original eigenpeak features associated with the aging degree in the Raman spectral dataset of the aging samples of the transformer oil-paper insulation, but also maintains an approximate spatial distribution of the features of the original dataset,, and the generated dataset with a Signal-to-Noise Ratio of 24.84 dB, which is improved by 32.11% compared to the original dataset; and at the same time, it also improves the diversity of generated samples, and enhances the generalisation ability, quantitative analysis ability and robustness of the aging diagnosis model based on deep learning. The experimental results show that the Raman spectral dataset generated using the Diffusion-Based WGANGP data augmentation model outperforms other data augmentation methods on several classification models, especially when combined with the ResNet-SVM classification model, in terms of Accuracy (0.997 4), F1 score (0.996 9), Recall (0.996 0) and Precision (0.998 0), which indicates that the improved data augmentation model can effectively solve the problem of the scarcity of samples of transformer aged insulating oil, and at the same time improves the classification model's ability to diagnose the aging state of the transformer quantitatively.