With the booming of the Internet of Things (IoT) and the speedy advancement of Location-Based Social Networks (LBSNs), Point-Of-Interest (POI) recommendation has become a vital strategy for supporting people’s ability to mine their POIs. However, classical recommendation models, such as collaborative filtering, are not effective for structuring POI recommendations due to the sparseness of user check-ins. Furthermore, LBSN recommendations are distinct from other recommendation scenarios. With respect to user data, a user’s check-in record sequence requires rich social and geographic information. In this paper, we propose two different neural-network models, structural deep network Graph embedding Neural-network Recommendation system (SG-NeuRec) and Deepwalk on Graph Neural-network Recommendation system (DG-NeuRec) to improve POI recommendation. combined with embedding representation from social and geographical graph information (called SG-NeuRec and DG-NeuRec). Our model naturally combines the embedding representations of social and geographical graph information with user-POI interaction representation and captures the potential user-POI interactions under the framework of the neural network. Finally, we compare the performances of these two models and analyze the reasons for their differences. Results from comprehensive experiments on two real LBSNs datasets indicate the effective performance of our model.