Rapid population increase and economic growth in eastern China has lead to the degradation of many water bodies in the region, such as Lake Taihu, the third largest freshwater lake in China. Using data from recent investigations, the correlations between algae (measured as chlorophyll-a) and water quality indices in Lake Taihu were described by multivariate statistical analyses, and the key driving factors for the lake eutrophication were identified by principal component analysis. Results revealed strong spatiotemporal variation in the correlations between algae and water quality indices, suggesting that the limiting factor for the dominant algae growth depends on seasonality and location and it is necessary to reduce both nitrogen and phosphorus inputs for a long-term eutrophication control in this hyper-eutrophic system. Water temperature was another important controlling factor for algal growth in the lake. Using principal component analysis, nutrient contaminations from anthropogenic and natural inputs were identified as the key driving factor for the water quality problems of the lake. Moreover, five principal components were extracted and characterized with high spatial and seasonal variations in Lake Taihu. The key driving factors were believed to influence spatial variations including heavily polluted areas located in the northern and northwestern parts of the lake, where many manufacturing factories were built and wastewater from domestic and industrial plants was discharged. Based on this analysis, attention should be paid to effective land management, industrial wastewater treatment, and macrophytic vegetation restoration to reduce the pollutant loads and improve water quality. Principal component analysis was found to be a useful and effective method to reduce the number of analytical parameters without notably impairing the quality of information in this study.