Concept cognition and knowledge discovery from network data are important research directions of machine learning and artificial intelligence under the network background， and have been introduced into the study of recommendation system. The existing recommendation methods based on concept lattice ignore the network structure relationship between nodes. At the same time， the efficiency of constructing concept lattice is low and the constraints of constructing concept set are strict， which is difficult to realize in large-scale social networks. In order to solve these problems， this paper integrates the topology of complex networks and weaken-concept similarity under the framework of network formal context， and proposes a group recommendation algorithm based on weaken-concept similarity. Firstly， the importance of attributes is described by defining attribute degree and attribute density， and then the expert nodes are determined by using the improved node influence. Secondly， the community is divided by expert nodes， the group recommendation research is carried out by using the lower limit similarity of attribute weaken-concept in the divided community， and then the recommendation rules are obtained and the group recommendation is applied to the corresponding communities. Finally， the influence of various parameters on the algorithm is analyzed on MovieLens and Filmtrust datasets， and reasonable values of the parameters are determined. After that， the proposed algorithm is compared with other recommended algorithms， and the experiments show that the proposed algorithm is effective.