Cyanobacteria bloom is one of the important characteristics of eutrophication in lake waters. The characteristics, hazards, and treatment methods of different bloom-forming cyanobacterial groups are significantly different. Therefore, in the process of implementing eutrophic lake pollution control, ecological restoration, and cyanobacteria ecological disaster prediction and early warning, how to quickly and accurately grasp the spatiotemporal distribution characteristics of different cyanobacteria groups have become an urgent scientific question. This study is based on the laboratory cultivation of pure algae species and indoor optical control experiments. Based on the inherent optical characteristics of the three main bloom-forming cyanobacterial groups: Microcystis, Dolichospermum, and Aphanizomenon, we screened the characteristic bands of the absorption and scattering spectra of different cyanobacterial groups and five nonlinear optimization quantitative identification models were constructed respectively. The model a-CIM_440,620,675 based on the absorption characteristic bands of 440, 620 and 675 nm shows the best performances. By applying field measured optical characteristics data to this model, quantitative monitoring of the main cyanobacterial group in Lake Chaohu was achieved, and the temporal and spatial distribution of the main bloom-forming cyanobacterial group in Lake Chaohu was analyzed. The results show that the cyanobacterial groups in Lake Chaohu are dominated by species of Microcystis and Dolichospermum. Microcystis generally occupies the western region of the lake in summer, whereas Dolichospermum dominated in cold seasons. In Lake Chaohu both of Microcystis bloom and Dolichospermum bloom are found in May. The non-algal bloom sections are mainly Microcystis and Dolichospermum, and they are both vertically and uniformly distributed. There are Microcystis and Dolichospermum in Lake Chaohu, and the higher concentration of cyanobacteria mainly exists below the water surface within 20 cm. This study can provide important theoretical basis and scientific support for the prediction and early warning of cyanobacteria blooms in eutrophic lakes and help with the decisions of relevant management departments.