Abstract: Domestic waste water is an important source of natural water pollution. The treated domestic waste water is discharged into natural water bodies through the pipe network,which has a serious impact on the quality of the receiving water body. The water quality monitoring data from Taihu Lake and the plant-resolved and actual operation data of 212 waste water treatment plants in Taihu Lake from 2007 to 2015 are collected and analyzed in order to accurately predict the total nitrogen in Taihu Lake. The Pearson correlation coefficient is used to calculate the relationship between Taihu Lake water quality and the WWTP effluent indexes. For the top five items with high correlation,three machine learning models,K-Nearest Neighbors（KNN）,Decision Tree,and AdaBoost are used to predict the monthly average TN in Taihu Lake. In general,AdaBoost has higher precision and better accuracy,with a goodness-of-fit index of 0.84 and a root mean square error less than 14.08%,which establishes a good mathematical model for predicting of TN concentration of Taihu Lake. Meanwhile,the model finds that NO₃-N,NH₄-N,TP in Taihu Lake and NH₄-N in effluent of WWTP can cause an important impact on the TN concentration of Taihu Lake.