Focused on the problem of inaccurate aquaculture water temperature and pH prediction, a mixed algorithm for water quality parameters prediction which was based on particle swarm optimization BP neural network (PSO-BPNN) was proposed. Firstly, the particle swarm optimization (PSO) algorithm was applied in calculating the initial weights and thresholds of BP neural network (BPNN).Secondly, the abnormal data were fixed andthe six parameters of water quality as inputs were used, the temperature and pH value of the next time point were used as outputs to establish aquaculture water quality prediction model. Finally, the collected water quality data were used to conduct training in BP neural network, and the feasibility and performance of water quality prediction model was tested through experiments. Compared with support vector regression (SVR) and normal BP neural network, in the aspect of predicting water temperature using PSO-BPNN, the decreasing amplitudes of RMSE were 64% and 80% respectively, while in the aspect of predicting pH value, the decreasing amplitudes of RMSE were 32% and 65% respectively. The results of experiments show that aquaculture water quality prediction model based on PSO-BPNN is flexible, simple, convenient and it also has a good capacity of prediction.