Link prediction is an important means of mining potential relationships between nodes in the future through known network topology and node attributes, which is an effective method for predicting missing links and identifying false links and has practical significance in the study of social network structure evolution. Traditional link prediction methods are based on the similarity of node information or path information. However, the former considers a single index, resulting in limited prediction accuracy, and the latter is not suitable for application in large-scale networks due to excessive computational complexity. Through the analysis of network topology, this study proposes a social network link prediction method based on the interacting degree of nodes (IDN). The method first introduces the concept of node efficiency based on the path characteristics between nodes in the network, which improves the accuracy of link prediction between nodes without common neighbors. In order to further explore the relevant attributes of common neighbors between nodes, by analyzing the topology of common neighbors between nodes, the method also innovatively integrates the path characteristics and local information to propose the definition of the IDN in a social network, which accurately portrays the degree of similarity between nodes and thus enhances the prediction ability of network links. Finally, this study validates the IDN method with the help of six real network datasets, and the experimental results show that, compared with the current mainstream algorithms, the method proposed in this study shows better prediction performance in both AUC and Precision evaluation indexes, and the prediction results have been improved by an average of 22% and 54%, respectively. Therefore, the proposal of node interaction degree has high feasibility and effectiveness in link prediction.