The prevention and management of cyanobacterial blooms is an important part of lake environment and water ecological management. Real-time acquisition of spatial distribution of cyanobacterial blooms is of great significance for timely salvage and disaster reduction. Aiming at the problems of time-consuming and laborious ground surveys, satellite remote sensing monitoring with a low spatial and temporal resolution, a new method for real-time monitoring of cyanobacterial blooms in lakes using video surveillance network (VSN) was proposed. Based on the 33 cameras of VSN around Lake Chaohu, the study focuses on the real-time and accurate extraction of cyanobacteria distribution information from video images. First, in order to overcome the challenges of different observation angles from different cameras, light intensities and background conditions, the representation of cyanobacterial blooms in video images was analyzed. Then, a multi-scale depth network was used for coarse-grained image classification to distinguish cyanobacteria from turbid and shadowed water; Random forest method was used to finely recognize cyanobacterial blooms to overcome the strong heterogeneity of cyanobacteria. Finally, the distribution information of cyanobacterial blooms was acquired. Based on the average daily and monthly cyanobacteria coverage of the coastal waters of the fishery administration station, monitoring of cyanobacterial blooms dynamics along the Lake Chaohu coast was completed, which can provide technical support for the management of cyanobacterial blooms.