With the development of network representation learning technology

researchers are increasingly considering the integration of multi-dimensional attribute information to enhance the performance of network representation. In view of the lack of conflict discrimination and evaluation index for multi-attribute feature fusion in existing network representation learning methods

this paper proposes a network representation learning fusion method based on D-S evidence theory. Firstly

the support degree of different attribute information to the fusion result is given by SVM algorithm. Then

the fusion evaluation index in network representation learning is calculated by using evidence combination rules

and the confidence degree of each attribute is fully considered based on confusion matrix. Simulation results on three types of data sets show that the method can effectively detect conflicts in network representation fusion and improve the performance of fusion representation.