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Point-of-Interest (POI)recommendation algorithm based on the Location-based social network (LBSN) is a hot research topic in recent years. Effective POI recommendation has great economic and social benefits. The POI recommendation model (MF-ADNN) which combines matrix factorization and deep learning with attention mechanism is studied, aiming at the data sparsity problem in LBSN, user’s fine-grained interest (i.e. long-term and short-term interest) sequence modeling problem and the POI recommendation problem of combining various influencing factors. On the one hand, feature matrix is constructed to alleviate the problem of sparse check-in data, and hidden factors are obtained by matrix factorization to calculate POI feature vectors; On the other hand, construct a sequence modeling method of user's fine-grained interest with attention mechanism, effective Learning long-term and short-term interest characteristics of users and improving the accuracy of POI recommendation. Combining the above two methods, the POI recommendation model that can integrate a variety of influencing factors is finally obtained. Through the comparison test, the recommended effect of the model is further verified.