CTR prediction serves as a valuable function to provide indications about the effectiveness of advertising campaigns. Numerous models have been developed to learn dynamic representations and sophisticated feature interactions for CTR prediction. We observe that users’ behavioral sequences have periodic patterns, which is a crucial factor for capturing the temporal dependency in highly dynamic environments such as online advertising. Unfortunately, existing work ignores periodic patterns in CTR prediction, and thus incurs the low model performance. This article proposes a periodic CTR prediction model in the GNNs modeling framework (PGNN), that combines periodic graph representations and feature graph representations. The former learns periodic graph representations of users’ and ads’ sequences with multi-scale periodic patterns and the high-order collaborative information across sequences on a dynamic graph of user-ad interactions, and the latter is designed to learn sophisticated feature interactions by incorporating the principle of field-aware feature interaction into an interpolable graph convolutional attention mechanism on a feature graph. Experiments conducted on three public datasets (i.e., Movielens-1M, Criteo-attribution, and Alimama) demonstrate the superiority of PGNN. PGNN outperforms the strongest baseline by 0.01–0.02 in terms of AUC and Logloss. Meanwhile, the effectiveness of periodic graph representation learning is also verified in this research.