The modeling of user-item interaction patterns is an important task for personalized recommendation. Many recommendation systems are based on the assumption that there is a linear relationship between users and items， and ignore the complexity and non-linearity of interaction between real and historical items， as a result， these systems cannot capture the complex decision-making process of users. Therefore， a more expressive top-N recommendation system’s item similarity factor model solution was combined with the multi-layer perceptron approach， to effectively model the higher-order relationships between items and capture more complex user decisions. The combination effect was verified on the three datasets of MovieLens， Foursquare and ratings_Digital_Music； and compared with the benchmark methods such as MLP （Multi-Layer Perception）， Factored Item Similarity Model （FISM）， DeepICF （Deep Item-based Collaborative Filtering） and ItemKNN （Item-based K-Nearest Neighbors）， the results demonstrate that the proposed method has significant improvement in recommendation performance.