Wavelet neural network is a new kind of hierarchical and multiresolution artificial neural network which based on wavelet analysis theory. In this paper, we choose an appropriate wavelet base and decompose scale to analysis chlorophyll-a of the West Lake. We divide the original sequence into a low frequency and several high frequency parts, then establish model Ⅰ and model Ⅱ for short-term prediction of chlorophyll-a concentration in the West Lake through BP neural networks. The model Ⅰ uses low frequency part only as input for network to forecast the content of chlorophyll-a, while the model Ⅱ uses the low frequency and the high frequency part as inputs, then summarizes the outputs to get the final product. Comparing with the two models, we can see the average error of model Ⅱ is smaller than of model Ⅰ,and the scope of error is also narrow. That means the precision and stability of model Ⅱ are higher than of the model Ⅰ. Finally, we forecast the water quality with the model Ⅱ. That shows the average relative error between predictive value and actual value is 6.4%. By selecting the third pot (Zhongshan dock) to generalize the model Ⅱ, which enable the average error is 6.9%. The result indicates that wavelet neural network can successfully forecast the content of Chlorophyll-a in the West Lake, so can provide scientific guidance for the West Lake management.