Phytoplankton is a crucial component of lake ecosystems, and understanding its species diversity and community composition is essential for grasping the functionality of lake ecosystems. In recent years, environmental DNA (eDNA) technology has increasingly been applied in ecological surveys. However, whether the results based on eDNA technology are in line with those obtained through traditional morphological identification for detecting phytoplankton remains unclear. Additionally, there is a lack of research on simultaneously detecting both eukaryotic and prokaryotic phytoplankton communities using eDNA technology. In this study, we compared the results obtained by eDNA technology and morphological identification in detecting the species diversity and community composition of phytoplankton (including both eukaryotic and prokaryotic ones) in in-situ mesocosms exposed to different intensities of grazing pressure. The results indicated that: (1) eDNA technology based on the 18S rRNA gene could obtain diversity of prokaryotic phytoplankton community, the trends in phytoplankton alpha diversity (species diversity and Shannon-Wiener index) obtained by eDNA technology were consistent with those by morphological identification in the in-situ mesocosms; (2) Compared with the results of morphological identification, eDNA technology based on the 18S rRNA gene and 16S rRNA gene detected more species, effectively highlighting differences in community composition among different treatment groups; (3) eDNA technology based on the 18S rRNA gene demonstrated higher precision in detecting phytoplankton diversity. Despite limitations in monitoring phytoplankton biomass, eDNA technology exhibited significant advantages over morphological identification in assessing phytoplankton biodiversity. This study attempted to apply eDNA technology to simultaneously detect both eukaryotic and prokaryotic phytoplankton, it confirms the higher resolution and precision of eDNA technology in detecting phytoplankton species diversity and community composition, providing more comprehensive data for phytoplankton monitoring. This provides a basis for the broader adoption of eDNA technology in the study of phytoplankton diversity and aquatic environmental monitoring.