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 was proposed. Geographic influence modeling and social influence modeling were performed on geographic information and social information, and temporal information and geographic information were combined to model temporal and spatial influence, and the three influence scores were integrated in a weighted summation manner to obtain user preference score. According to the user preference score, each user was 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. In addition to the user check-in frequency, the 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, which provides certain reference value for the research of point-of-interest recommendation integrating key information.