Network embedding has been a popular branch of deep learning, which represents the network information by mapping network nodes to an extended low-dimensional space. According to user co-tag network and social network, we employed network representation learning to extract user representation vector, respectively, and proposed a novel service recommendation method. Here using user’s representation vector learned to calculate the similar users, and recommends appropriate services to the target users according to the preferences of top-k similar users. To investigate the feasibility of our approach, experiments were carried out on two open data sets, Delicious and Last.FM. The results showed that our method outperforms the four benchmarks, with an average improvement of 13% in precision, 18.6% in recall and 13.1% in F-measure. It is also found that when learning user representation, the co-tag relationship between users is as important as the social relationship. Meanwhile, during the process of collaborative recommendation, the number of similar users returned for a target user is suitable in the 25and 30 range.