Generative Moment-Matching Network (GMMN) is a deep generative model, which employs maximum mean discrepancy as the objective to learn model parameters. However, this model can only generate samples, failing to infer the latent code from samples for downstream tasks. In this paper, we propose a novel Joint Generative Moment-Matching Network (JGMMN), which learns the structural latent code for unsupervised inference. Specifically, JGMMN has a generation network for the generation task and an inference network for the inference task. We first reformulate this model as the two joint distributions matching problem. To solve this problem, we propose to use the Joint Maximum Mean Discrepancy (JMMD) as the objective to learn these two networks simultaneously. Furthermore, to enforce the consistency between the sample distribution and the inferred latent code distribution, we propose a novel multi-modal regularization to enforce this consistency. At last, extensive experiments on both synthetic and real-world datasets have verified the effectiveness and correctness of our proposed JGMMN.