Community detection and link prediction are hot issues in network data research. Taking into account both network transitivity and block structure can help capture the effective association between individuals and detect the inherent patterns in the data, thus helping researchers explore more data values and make decisions. Most of the current algorithms and models focus on single-level analysis of network transitivity or block structure, and they rely on certain assumptions This study proposes a network embedding stochastic blockmodel (NE-SBM) for community detection and link prediction. A Bayesian framework is built to regularize the model parameters, and the Metropolis Hasting-Gibbs algorithm is applied to obtain the hidden location and community affiliation represented by node embedding. The study also takes advantage of the multidimensional scaling algorithm to solve the hidden location identifiability problem. The proposed method can solve the problem of over-reliance on judgment criterion or evaluation function in traditional heuristic algorithms and has better adaptability to all types of data. In addition, the experimental results on artificial and real data further validate the superior performance of the method in community detection and link prediction.