In social networks, people tend to visit places that are more interesting and close to themselves. Point-of-interest recommendation based on user’s interest preferences can effectively help users choose places which they are interested in. In this paper, a point-of-interest recommendation method based on location and time information was proposed. Three steps from the perspective of the point-of-interest were involved. Firstly, the similarity between the user history access point-of-interest and the point-of-interest that the user has not visited was calculated by using the location information of the point-of-interest accessed by the user. Then, the day is divided into different time periods, during which the number of times all points of interest are checked in is counted. The similarity between the user’s historical visit of point-of-interest and the point-of-interest that the user has not visited was calculated by using the time information. Finally, the similarity between the user history access point of interest and the user’s non-visited point-of-interest was calculated according to the location and time information of the point-of-interest. The point-of-interest that the user has not visited was recommended according to the Top-N policy. Experimental verification was carried out on the real data set in the real society. The experimental results show that the proposed method is effective.