In order to solve the problem of exponential increase of label space, an overlapping community discovery algorithm based on latent feature was proposed. Firstly, a generative model for network including overlapping communities was proposed. And based on the proposed generative model, an optimal object function was presented by maximizing the generative probability of the whole network, which was used to infer the latent features for each node in the network. Next, the network was induced into a bipartite graph, and the lower bound of feature number was analyzed, which was used to optimize the object function. The experiments show that, the proposed overlapping community discovering algorithm can improve the recall greatly while keeping the precision and execution efficiency unchanged, which indicates that the proposed algorithm is effective with the exponential increase of label space.