In location-based social networks (LBSN), users can check-in at points of interest (POI) to record their trips. POI recommendation is an important service provided by LBSN; it can help users quickly find POI of interest, and also help POI providers more comprehensively understand user preferences and improve service quality. This paper proposes a POI recommendation algorithm that is based on attention mechanism. The sequence characteristics and short-term preferences of historical data are captured through the attention mechanism module and long short-term memory network (LSTM), and the POI location prediction is carried out in combination with the user embedding characteristics, and a better prediction accuracy is obtained. These results simulated show that the proposed method can realize the reliable analysis of complex data sets, and its precision index remains above 0.1 and recall index remains above 0.08, and it can also alleviate the cold start problem and better meet the personalized needs of users.