Algal bloom, which results in significant adverse effects on aquatic ecosystem health, drinking water safety, and human beings, is one of the most serious environmental problems in lakes. Since many factors, such as non-point and point source pollution, meteorological and hydrodynamic conditions, and morphological features and characteristics of the lake ecosystem itself, can influence the outbreak of algal bloom, its mechanism is very complex and highly uncertain. In particular, large water bodies such as Lake Taihu have eco-environmental conditions with significant spatial and temporal variations. In the study, Lake Taihu was selectedand continuous monthly (2008-2010)on-site (33 sites) monitoring data were used. The self-organizing map (SOM) neural network approach was applied to automatically evaluate the algal bloom status according to long-term on-site monitoring data of the entire Lake. Then, for different intensities of algal bloom, the spatial and temporal distribution and variation of environmental and ecological factors (Chlorophyll-a, water temperature, COD_Mn, TN, TP, main algae composition) were analyzed. The relation between the intensity of water bloom and the environmental and ecological factors were assessed. The intensity of algal blooms in Taihu Lake was classified into four degrees, no, light, moderate, and severe water blooms. The spatial-temporal occurrence of algal bloom in Lake Taihu with different intensity was clearly different. Spatially, the algal bloom intensity of Lake Taihu decreased from the northwest to the southeast. The most severe bloom occurred in the north and northwest areas, which is the main entrance of major rivers flowing into Lake Taihu. Moderate bloom occurred in the north, west, and southwest areas but seldom occurred in the east and central areas. Light bloom appeared across the entire lake, except for the southeast. Temporally, the most severe bloom outbreaked occurred during July to October. Moderate, light, and no blooms appeared from April to November. For different degrees of algal blooms, the corresponding environmental-ecological variables of chlorophyll-a, water temperature, COD_Mn, TN, TP, and main algae composition (Cyanobacteria, Chlorophyta, Bacillariophyta) were clearly varied. The relations between these environmental-ecological variables were very complex. Generally, water temperature and the concentration of chlorophyll-a, COD_Mn, TN, and TP increased from no algal bloom to severe algal bloom. For all the algal blooms, distinct variations were observed among the concentrations of chlorophyll-a and TP, while there were no marked differences among the water temperature, COD_Mn, and TN. In relation to phytoplankton communities, cyanobacteria was dominant in all the algal blooms with different intensities. These findings are not only important for comprehensively understanding the spatial-temporal variations of algal bloom in Lake Taihu, but also support further identification of the mechanisms of algal bloom. In addition, this study might help the government and related decision-makers in establishing policies and practices on algal bloom monitoring and prevention.