Exploring the temporal variation characteristics of phytoplankton community structure is of great significance for understanding the evolution of aquatic ecological environment; The automatic monitoring of phytoplankton communities provides fundamental data for revealing the multi-scale changes in phytoplankton community structure. However, the multi periodicity of high-frequency monitoring data also poses challenges to traditional time series analysis methods. This study takes the Jiangdong Reservoir in Beixi, Jiulong River as a case study, and obtains hourly automatic monitoring data of the biomass of four groups of green algae, blue-green algae, diatoms, and cryptoalgae from 2017 to 2022. Using Multiple Seasonal-Trend decomposition using LOESS, the biomass of each group is decomposed into four components: trend, annual period, daily period, and residual. The multi time scale variation characteristics of community structure are analyzed. The results showed that: (1) The biomass of the green algae group rapidly increased from 2020 and reached its peak in 2021, becoming the dominant group; The overall trend of other groups is declining. (2) The seasonal fluctuations of various groups are relatively stable, with cryptic algae having the largest amplitude, followed by green algae and blue-green algae, and diatoms having the smallest amplitude. (3) All types of groups have stable diurnal rhythms, reaching their peak before 12-16 pm, with green algae showing the largest amplitude and blue-green algae showing the smoothest changes. (4) Seasonal cycles and long-term trends jointly explain the main part of community abundance variation, while the contribution of daily cycles is relatively small. The research results indicate that the changes in green algae and diatom groups are driven by both multi-year background and seasonal factors, while the fluctuations in blue-green algae and cryptoalgae groups are more dependent on seasonal temperature light cycles. This study reveals the dominant substitution and niche segregation phenomena of phytoplankton at multiple scales including interannual, seasonal, and diurnal, which can provide scientific basis for early warning of harmful algal blooms and management of aquatic ecosystems.