State-of-the-art NLP models have demonstrated exceptional performance across various tasks, including sentiment analysis. However, concerns have been raised about their robustness and susceptibility to systematic biases in both training and test data, which may lead to performance challenges when these models encounter out-of-distribution data in real-world applications. Although various data augmentation and adversarial perturbation techniques have shown promise in tackling these issues, prior methods such as word embedding perturbation or synonymous sentence expansion have failed to mitigate the spurious association problem inherent in the original data. Recent counterfactual augmentation methods have attempted to tackle this issue, but they have been limited by rigid rules, resulting in inconsistent context and disrupted semantics. In response to these challenges, we introduce a diffusion-based counterfactual data augmentation (DCA) framework. It utilizes an antonymous paradigm to guide the continuous diffusion model and employs reinforcement learning in combination with contrastive learning to optimize algorithms for generating counterfactual samples with high diversity and quality. Furthermore, we use a dual sentiment classifier to validate the generated antonymous samples and subsequently perform sentiment classification. Our experiments on four benchmark datasets demonstrate that DCA achieves state-of-the-art performance in sentiment classification tasks.