In order to solve the problem of data sparseness in the task of point-of-interest recommendation and make full use of the diverse information in the location-based social network to further improve the quality of personalized recommendation, a point-of-interest recommendation algorithm integrating multiple impact factors is proposed. The algorithm performs geographic influence modeling and social influence modeling on geographic information and social information, and combines temporal information and geographic information to model temporal and spatial influence, and then integrates the three influence scores in a weighted summation manner to obtain user’s preference score. According to the user’s preference score, each user is provided with a recommendation list containing Top-N points of interest. The experimental results show that on the two public datasets, the point-of-interest recommendation model that integrates multiple impact factors performs better than the baselines. Therefore, in addition to user’s check-in frequency, geographic-social-spatial-temporal influence is also a key part of the point-of-interest recommendation task, and the modeling of these three influences is of great significance. The proposed algorithm provides a certain reference value in the research of point-of-interest recommendation that integrates key information.