Research on microecology has been carried out with broad perspectives in recent decades, which has enabled a better understanding of the gut microbiota and its roles in human health and disease. It is of great significance to routinely acquire the status of the human gut microbiota; however, there is no method to evaluate the gut microbiome through small amounts of fecal microbes. In this study, we found ten predominant groups of gut bacteria that characterized the whole microbiome in the human gut from a largesample Chinese cohort, constructed a real-time quantitative polymerase chain reaction (qPCR) method and developed a set of analytical approaches to detect these ten groups of predominant gut bacterial species with great maneuverability, efficiency, and quantitative features. Reference ranges for the ten predominant gut bacterial groups were established, and we found that the concentration and pairwise ratios of the ten predominant gut bacterial groups varied with age, indicating gut microbial dysbiosis. By comparing the detection results of liver cirrhosis (LC) patients with those of healthy control subjects, differences were then analyzed, and a classification model for the two groups was built by machine learning. Among the six established classification models, the model established by using the random forest algorithm achieved the highest area under the curve (AUC) value and sensitivity for predicting LC. This research enables easy, rapid, stable, and reliable testing and evaluation of the balance of the gut microbiota in the human body, which may contribute to clinical work.