The recommendation algorithm can break the restriction of the topological structure of social networks, enhance the communication power of information (positive or negative) on social networks, and guide the information transmission way of the news in social networks to a certain extent. In order to solve the problem of data sparsity in news recommendation for social networks, this paper proposes a deep learning-based recommendation algorithm in social network (DLRASN). First, the algorithm is used to process behavioral data in a serializable way when users in the same social network browse information. Then, global variables are introduced to optimize the encoding way of the central sequence of Skip-gram, in which way online users’ browsing behavior habits can be learned. Finally, the information that the target users’ have interests in can be calculated by the similarity formula and the information is recommended in social networks. Experimental results show that the proposed algorithm can improve the recommendation accuracy.