Abstract: The Click-through rate problem based on feature interaction modeling method has been widely explored and has made great progress. It can alleviate the loss of effective information, but to a certain extent, it depends on the co-occurrence of different features, and there is a problem of feature sparseness. Therefore, in order to solve the problem that the feature representation cannot be learned efficiently because of the few occurrences of the interactive process features, a click-through rate prediction model LGCDFM (LightGCN with DeepFM) based on LightGCN enhanced embedding layer learning is proposed. In the initial embedding layer, a divide-and-conquer learning strategy is adopted. Different types of nodes are distinguished in the graph structure. The information of the same types of nodes is first transmitted to ensure the frequency of features, and then the information of multi-hop neighbors is captured by the interaction between different types of high-order connected nodes. LightGCN structure has powerful feature extraction and representation learning capabilities, and it discards feature transformations and nonlinear activation functions that are not conducive to interaction. It becomes the advantage of collaborative filtering tasks for processing simple user-item interaction data, effectively reducing feature sparsity problem. Finally, it means that the learning layer applies the classic model of click-through rate prediction DeepFM to end-to-end learning high-order and low-order feature combinations, and learns from sparse data by latent vectors to improve the performance of click-through rate prediction tasks. Experiments on two public datasets of Criteo and Avazu show that the performance of this model is better than existing methods in terms of click-through rate prediction and feature sparseness problems.