Predicting click-through rate (CTR) is a fundamental task in online advertising and recommendation systems. Mainstream models often enhance performance and generalization by modeling interactions between high-order and low-order features. However, many models only learn fixed representations of each feature, neglecting the importance of features in different contexts and having overly simplistic model structures. To address these issues, this study proposes the feature refinement convolutional neural network-fusion matrix factorization (FRCNN-F) model. Firstly, the study integrates the feature generation module of convolutional neural networks into the feature refinement network (FRNet), leveraging its ability to generate new features by recombining local patterns to enhance important feature selection. Secondly, the study designs the fusion matrix factorization mechanism to enable the model to perceive context and model displays through interactions across different scenarios, thereby enhancing the combination of submodels. Finally, through comparative experiments on the publicly available datasets Frappe and MovieLens, the results demonstrate that the FRCNN-F model outperforms the baseline FRNet, with improvements of 0.32% and 0.40% in AUC scores and reductions of 1.50% and 1.11% in cross-entropy loss (Logloss) respectively. This research has practical applications in achieving precise advertising and personalized recommendations.