With the rapid development of edge intelligence in wireless communication networks, mobile edge networks (MEN) have been broadly discussed in academia. Supported by considerable geographical data acquisition ability of mobile Internet of Things (IoT), the MEN can also provide spatial locations-based social service to users. Therefore, suggesting reasonable points-of-interest (POIs) to users is essential to improve user experience of MEN. As the simple user-location data is usually sparse and not informative, existing literature attempted to extend feature space from two perspectives: contextual patterns and semantic patterns. However, previous approaches mainly focused on internal features of users, yet ignoring latent external features among them. To address this challenge, in this paper, a deep distributed learning-based POI recommendation (Deep-PR) method is proposed for situations of MEN. In particular, hidden feature components from both local and global subspaces are deeply abstracted via representative learning schemes. Besides, propagation operations are embedded to iteratively reoptimize expressions of the feature space. The successive effect of the above two aspects contributes a lot to more fine-grained feature spaces, so that recommendation accuracy can be ensured. Two types of experiments are also carried out on three real-world datasets to assess both efficiency and stability of the proposed Deep-PR. Compared with seven typical baselines with respect to four evaluation metrics, obtained results of the overall performance of the Deep-PR are excellent. IEEE