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The edge sign prediction is to mine the sign-related implicit information according to the network topology, aiming to reveal the potential relationship between users. Node status and similarity can better represent sign attributes of edges, providing a theoretical basis for improving the prediction effect. By investigating the strong correlation between the two theories and the sign attributes of the edges, this paper established a sign prediction model. Firstly, it used prestige evaluate the social status of user nodes. At the same time, cosine similarity could represent the user's social preferences . Then, both sides were combined based on the logistic regression learning model to establish the edge sign prediction model LR-SN. Finally, a random gradient ascent algorithm would optimize the model during training. The experimental results of three real network datasets show that compared with the existing baseline methods, the accuracy of sign prediction of LR-SN model is significantly improved and has certain generalization, indicating that the fusion of local information and global information can further improve the prediction effect. [ABSTRACT FROM AUTHOR]