The auxiliary information in recommendation systems can provide real help for recommendation, while the traditional collaborative filtering algorithm has a low utilization rate of the auxiliary information and high data sparsity in the calculation of user similarity, which leads to low recommendation accuracy. In response to this problem, this study proposes an improved collaborative filtering algorithm that integrates user preferences and multi-interactive neural networks (NIAP-CF). Firstly, the information about the item attribute preferences of users is collected according to the rating matrix and the item attribute feature matrix. Then, the SBM method is used to calculate the similarity of item attribute preferences between users to improve the calculation formula for user similarity. In the process of score prediction, we build a multi-interactive neural network prediction model integrating user and item attribute preference. Dynamic trade-off parameters are introduced to integrate the predicted scores calculated by user similarity and by the model. Experimental verification based on the MovieLens data set shows that the improved algorithm can improve the recommendation accuracy and reduce the MAE and RMSE of score prediction.