With the advance of artificial intelligence and communication technology in the smart city, various location-based data of users can be collected via location-based social networks (LBSNs). How to make full use of these data for accurate point-of-interest (POI) recommendation is challenging because POI selection is influenced by various factors. In this article, we propose a network representation learning-enhanced multisource information (MSI) fusion model for POI recommendation in the context of LBSNs. The proposed model jointly considers various factors, including user preference, geographical influence, and social influence for a recommendation. Specifically, the social influence is modeled by performing network representation learning methods on the constructed co-visiting user networks so that the hidden complex social relationships among users can be measured automatically. Moreover, considering the significance of user preference and geographical influence, a fusion model is designed to jointly consider user preference, social influence, and geographical influence for POI recommendation. Our method is evaluated based on two publicly available data sets and extensive experimental results demonstrate that the proposed MSI fusion model outperforms several state-of-the-art algorithms for POI recommendation in terms of precision, recall, and F1.