Abstract: To reduce the influence of implicit feedback, data sparsity and content diversification on the recommendation algorithm of interest points and improve the accuracy of recommendation, a point of interest (POI) recommendation algorithm based on sequence mining was proposed in this paper. First, in the data preprocessing stage, the negative sampling method was used to generate data that does not exist in the data set as negative samples. Then, the matrix decomposition method was used to learn the implicit feature vectors of users and locations, and arrange candidate recommendation points according to the relationship between sites.Experiments on two POI access sequences were implemented on the open dataset FourSquare and Gowalla. Results show that the accuracy of the algorithm is much higher than that of the traditional method.