Identifying the relationship between the gut microbial community and the host environment, as well as the driving mechanism, are the key tasks in gut microbial research. Microbiome high-throughput gene sequencing and big data analysis are currently the mostly used techniques for investigation microbial communities. Existing studies on human gut microbiota data mainly focus on the community diversity and composition, while methods for deep exploration of the ecological and functional relationships among bacteria species are still lacking. An urgent task is therefore raised on developing computational methods to explore the interaction pattern between gut microbial components from in the sense of molecular network from microbiome sequencing data. In this paper, we adopt the network embedding method proposed in machine learning as a remediation to the drawbacks of traditional biological network learning technology which were solely dependent on the direct correlation between nodes, with stronger power in capturing the heterogeneity, hidden variables and imbalance features in microbial network interactions. By analyzing the correlation between the created function modules with the new approach and the environmental variables as well as key metabolite components, it is confirmed that the derived functional modules managed to identify biological-relevant feature that can be less recognized with previous approaches, which are helpful for further modeling of the potential coupling mechanisms among the biological systems. The method described in this article not only provides a new perspective for the analysis of gut microbial community structure, but also can be extended to other environmental microbiology research and reflect the driving process of community structure through multi-level information of data.