Quality of service (QoS)-aware cloud API recommendation systems play an important role in solving cloud API overload problems, differentiating cloud API performance, and achieving high-quality cloud API selection. However, due to the openness of the network environment and the monetary nature of cloud APIs, recommendation systems are susceptible to poisoning attacks, which causes the recommendation results to deviate from fairness and credibility. Existing defense methods against poisoning attacks mainly adopt the “detection and defense” strategy, which utilizes detection algorithms to filter out malicious users before model training to mitigate the influence of the attacks. However, due to the performance limitations of detection algorithms, it is inevitable that malicious users cannot be completely filtered out. To this end, this study proposes a continuous defense method against poisoning attacks on the QoS-aware cloud API recommendation system from a “learning to defense by attacks” perspective with trusted data augmentation. First, this study establishes a defense framework against poisoning attacks based on trusted data augmentation and enhances the robustness of the recommendation system by generating high-quality trusted user data for model training. Second, the study designs a trusted user generation algorithm based on the diffusion model, which employs iterative denoising to learn real-world QoS data distribution related to cloud APIs and generate high-quality trusted user vectors, thus mitigating the influence of data subjected to poisoning attacks on training models. Finally, extensive experiments are conducted based on real-world cloud API QoS datasets, and 11 recommendation algorithms from three categories are utilized to comprehensively evaluate the effectiveness and universality of the proposed defense method. Experimental results indicate that the proposed framework of continuous defense against poisoning attacks based on trusted data augmentation is effective, and the generated trusted user can significantly improve the robustness of the cloud API recommendation system.