In social networks, people tend to visit places that are more interesting and close to themselves, and point of interest recommendations are based on the user's interest preferences to effectively help users choose places which they are interested in. In this paper, proposing a point of interest recommendation method based on location and time information. This method is divided into three steps from the perspective of the point of interest. Firstly, Calculating the similarity between the user history access point of interest and the point of interest that the user has not visited by using the location information of the point of interest accessed by the user; then the time information is used to make the day divided into different time periods, counting the number of times all points of interest are checked in at different times of the day, calculating the similarity between the user's historical visit interest points and the points of interest that the user has not visited; finally, according to the location and time information of the points of interest comprehensively calculates the similarity between the user history access point of interest and the user's non-visited point of interest, and recommends the point of interest that the user has not visited according to the Top-N policy. Experimental verification is carried out on the real data set in the real society. The experimental results show that the proposed method is effective.