Understanding the status of gut microbiota has been recognized as crucial in health management and disease treatment. To meet the demands of medical and biological applications where rapid evaluation of gut microbiota in limited research environment is essential, we developed new sensing systems able to readout the overall characteristics of complex microbiota. Response patterns generated by a synthetic library of 12 charged block-copolymers with aggregation-induced emission units were analyzed with pattern recognition algorithms, allowing to identify the species/phyla of 16 axenic cultures of intestinal bacterial strains. More importantly, our method clearly classified artificial models of obesity-associated gut microbiota, and further succeeded in detecting sleep disorders in mice through comparative analysis of the normal/abnormal mouse gut microbiota. Our techniques can analyze complex bacterial samples far more quickly, simply and inexpensively than common metagenome-based methods, offering a powerful and complementary tool for gut microbiome analysis for practical use, e.g., in clinical settings.