Abstract: A personalized comment recommendation method based on a link prediction model of a graph bidirectional aggregation network. In a user-comment bipartite graph, comment features are aggregated into a user feature. A social network is used to fuse a neighbor feature of a user to obtain an embedding representation of the user. The embedding representation of the user is aggregated into a comment after an original feature of the user is removed, and the embedding representation of the user is adjusted based on a difference before and after comment aggregation. On this basis, a forwarding network is used to calculate a score of an edge based on an inner product of user node features at both ends of the edge, and finally make a recommendation based on the score. Furthermore, a recommendation system converts a comment recommendation task into a link prediction task between users in a small range.