Link prediction is a crucial task in network analysis that explores interactions between entities and forecasts new potential relationships in evolving networks. However， link prediction may generate biases， especially concerning links between entities with sensitive attributes. For example， the issue of “filter bubble”， which amplify the isolation of publicly accessible information and reduce diversity for online users. To address these challenges， ALFLP （Adding Link and Adversarial Learning for Fair Link Prediction） method was proposed after combining preprocessing stage methods and processing stage methods for addressing “filtering bubble” issue from the perspective of algorithmic fairness. In preprocessing stage， by adding links to disadvantaged link groups， the difference in link density between different groups was reduced. In processing stage， the output of the preprocessing stage was input into the adversarial learning-based method， and the generator and discriminator were played together to promote more inter-group links， thereby alleviating the “filter bubble” situation. Experimental results on real datasets pokec_n and pokec_z show that compared with baseline methods such as Jaccard， ALFLP method has the AUC index improved by about 12 and 10 percentage points respectively， and the modred index improved by about 0.14 and 0.10 respectively. It can be seen that ALFLP method can achieve a good balance between fairness and prediction accuracy.