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Water quality prediction is an important content of water pollution prevention and control, aiming at the problems such as low accuracy and slow convergence of traditional water quality prediction methods. In this paper, the Symlets and Daubechies wavelet systems are selected as the wavelet function, and the original data are denoised and compared. Combined with the advantages of RBF, Elman neural network and support vector machines, three different prediction models are optimized by different algorithms, a combined forecasting method based on Adaboost algorithm is proposed to optimize RBF, Elman neural network and support vector machine. Taking Beihai as the object to carry on the simulation experiment, the effectiveness of the method based on Adaboost"s dissolved oxygen combination is verified and compared with the prediction results of the single model. The results show that the proposed method is improved compared with the traditional single model, which provides a new idea for the accurate prediction of water quality.