Water is an indispensable source of human being and other living species, thus it has significant value of social economy and ecosystem to establish water quality prediction model. This study developed a W-LSTM time series model to predict water quality based on wavelet decomposition and LSTM. Daubechies5 (db5) wavelet was used to decompose water quality data series into high frequency and low frequency signals, and these signals were used as the inputs of LSTM model to train the model to predict water quality data. Four water quality indices (pH, DO, CODMn, and NH₃N) collected from the Wangjiaba River basin in Funan, Anhui Province, China were used to train, validate, and test the model. The training and prediction results of the model were compared with these results of the traditional LSTM neural network model. The results show that the proposed model is superior to the traditional LSTM model in a variety of evaluation indicators. It is proved that this method has higher prediction accuracy and generalization ability and it is a more effective modeling and prediction approach.