The water depth at the end of the tributary backwater area in the Three Gorges Reservoir (TGR) varies greatly, coupled with the habitat heterogeneity caused by complex hydrodynamic changes, shaping the characteristics of water bloom outbreaks that are different from shallow lakes. Based on the online monitoring system deployed in four tributaries of the TGR, this study uses wavelet transform (WT) and long short-term memory network (LSTM) to build the time-series forecasting model of algal kinetics, and discusses the optimal combination of key parameters such as the number of neural network layers, the number of hidden neurons in each layer, and the time steps. The results show that: WT-LSTM model can effectively predict the change of chlorophyll-a concentrations in four tributaries, the root mean square error (RMSE) is 0.049-0.221 μg/L, and the mean relative error (MRE) is 0.43%-1.12%. This study confirms that the deep learning model can learn inherent patterns of high-frequency monitoring data, and the mean RMSE and MRE in the four tributaries are decreased by 9.20% and 3.06%, respectively. After online data processing with the wavelet transform, the prediction performance of WT-LSTM is also better than WT-DBN, and the mean RMSE and MRE decreased by 51.72% and 59.24%, respectively. Comparison experiments with different time steps confirm that the accuracy of the model decreases with the improvement of the prediction time. While the mean relative error of the prediction task within 24 hours is less than 13%, and the prediction ability of the model to chlorophyll-a concentration of the interval maximum is better than the average. This study provides a research example for the combination of automatic monitoring data and deep neural network models to forecast harmful algal blooms. Through cross-validation experiments on data from four sites, it is confirmed that statistically relevant data can be extended for model training and testing samples, enhancing the stability of machine learning models in practical applications.