Abstract：We collected the meteorological and water quality data of Pingtan Thirty-six Feet Lake from January 2016 to May 2017 and developed a model based on the BP artificial neural network. Environmental factors were used as input indicators to run the model, while the chlorophyll-a concentration was used as an output index. 80% historical data were randomly extracted from the sample data for model calculating, and others were used for model verification. The results showed that the fitting degree of chlorophyll-a concentration and monitoring data reached R2=0.97, RMSE=0.05 μg/L, RSR=0.17, and the error was small when the temperature, conductivity and water temperature were taken as input factors. We sampled the lake for five times from March 13th to April 26th, 2019 by comparing the measured chlorophyll-a concentration with the predicted chlorophyll-a concentration. It showed that the RSR was 0.24, the deviation between measured and predicted values was small. The results showed that the model was expected to be applied to the prediction of chlorophyll-a concentration and early warning of water blooms in the area of Pingtan Thirty-six Feet Lake, which would provide a reference for the prevention and control of eutrophication.