Concerning the high time and space complexity caused by using high-dimensional tenuous vectors to represent network information in tenuous subgraph finding problem, a Tenuous subGraph Finding (TGF) algorithm based on network embedding was proposed. Firstly, the network structure was mapped to the low-dimensional space by the network embedding method in order to obtain the low-dimensional vector representation of nodes. Then, the tenuous subset finding problem in the vector space was defined, and the tenuous subgraph finding problem was transformed into the tenuous subset finding problem. Finally, the sample points with lowest local density were searched iteratively and expanded to figure out the largest tenuous subset satisfying the given conditions. Experimental results on Synthetic_1000 dataset show that, the search efficiency of TGF algorithm is 1 353 times that of Triangle and Edge Reduction Algorithm (TERA) and 4 times of that Weight of K-hop (WK) algorithm, and it achieves better results in k-line, k-triangle and k-density indexes