Click-through Rate (CTR) prediction is a crucial task in online advertising and recommender systems. Theoretically, proper ensemble of multiple different CTR prediction models can improve the prediction effectiveness. Unfortunately, most of the existing ensemble learning methods for CTR prediction are designed for cross-sectional data and neglect user historical behavior sequence which is important for predicting user's future click behavior. To address the above issues, we propose a Hypergraph Diffusion-based Sequential Ensemble framework for CTR prediction (HDSE). Specifically, considering the inherent capability of diffusion models in space exploration, we design a generalized conditional diffusion model, which adaptively identifies critical information from diverse sequential models, to capture user's dynamic interest evolution across diverse contexts. To avoid corrupting the item dependencies caused by isotropic Gaussian noise used in traditional diffusion models, we construct a behavior hypergraph and design an anisotropic hypergraph-based smoothing operator to inject structure-aware noise for better exploring the user's interest space. For large-scale application scenarios, we propose a computationally efficient approximation method for estimating hypergraph propagation matrix in the smoothing operator. Extensive experiments on three real-world datasets demonstrate the effectiveness of the proposed model.