Abstract: How to improve the click through rate of advertisement is a challenge to the network marketing in the era of big data. Considering the uncertainty of users click behavior, a click-through rate prediction model based on product attention interactive network model is proposed. The model made the inner or outer product of the user's behavior vector, and gave the corresponding weight to the interactive vector according to the characteristics of advertising itself. Experiments were carried out on two data sets. The results show that the proposed model can improve the normalized Gini coefficient by more than 2% compared with the traditional hit rate prediction model, and can predict more accurately.