Despite the phased success of cyanobacterial bloom management in eutrophic shallow lakes worldwide, cyanobacterial bloom outbreaks are still frequent. Currently, the kinetic mechanism of cyanobacterial growth is still unclear, and the frequency of data in most studies is measured in days or months, which has difficulties revealing the real growth state of cyanobacteria. Therefore, it is important to explore the growth pattern of cyanobacteria in the perspective of high frequency data for the prevention and control of cyanobacterial blooms. In this study, an indoor high-frequency monitoring experiment was designed to systematically analyze the growth kinetics of Microcystis aeruginosa (M. aeruginosa), a typical dominant cyanobacterial species, with monitoring data frequency up to 15 min/time. High-frequency monitoring experiments found that the chlorophyll-a (chla) concentration of M. aeruginosa in light and dark environments had obvious differences, which were summarized and divided into Light adaptation period (LAP), Logarithmic growth period (LGP) and Stabilization period (SP). In LAP (0-20 days), the chlorophyll-a (chla) concentration of M. aeruginosa in the dark environment was higher than in the light environment. In LGP (20-45 days), M. aeruginosa showed logarithmic growth and chla concentrations in the light environment exceeded those in the dark environment. In SP (45-80 days), the chla concentrations in light and dark environments were almost the same, and the population of M. aeruginosa stopped growing and reached the limit of population density (k). It was verified that the three stages of growth of M. aeruginosa found in this study coincided with the Logistic growth and reflected its rationality. In addition, the three stages were found in the context of high-frequency data, reflecting both the growth pattern of the algae in light and dark environments and the maximum instantaneous growth rate (2/k) and growth extremes of M. aeruginosa. This finding can help reveal the periodic growth characteristics and patterns of cyanobacteria in more detail and clearly and can provide new ideas for the prediction and management of cyanobacterial blooms in shallow eutrophic lakes.