Latent Factor Model （LFM） have been widely used in recommendation field due to their excellent performance. In addition to interactive data， auxiliary information is also introduced to solve the problem of data sparsity， thereby improving the performance of recommendations. However， most LFMs still have some problems. First， when modeling users by LFM， how users make decisions on items based on their feature preferences is ignored. Second， the feature interaction using inner product assumes that the feature dimensions are independent to each other， without considering the correlation between the feature dimensions. In order to solve the above problems， a recommendation model for User Attribute preference Modeling based on Convolutional Neural Network （CNN） interaction （UAMC） was proposed. In this model， the general preferences of users， user attributes and item embeddings were firstly obtained， and then the user attributes and item embeddings were interacted to explore the preferences of different attributes of users to different items. After that， the interacted user preference attributes were sent to the CNN layer to explore the correlation between different dimensions of different preference attributes and thus obtain the users’ attribute preference vectors. Next， the attention mechanism was used to combine the general preferences of the users with the attribute preferences obtained from CNN layer to obtain the vector representations of the users. Finally， the dot product was used to calculate the users’ ratings of the items. Experiments were conducted on three real datasets： Movielens-100K， Movielens-1M and Book-crossing. The results show that the proposed algorithm decreases the Root Mean Square Error （RMSE） by 1.75%， 2.78% and 0.25% respectively compared with the model of Neural Factorization Machine for sparse predictive analytics （NFM）， which verifies the effectiveness of UAMC model in improving the accuracy of recommendation in the rating prediction recommendation of LFM.