Abstract: Dissolved oxygen is an important environmental factor that affects the growth of aquatic organisms and water environment. Accurate prediction of dissolved oxygen is beneficial to the healthy development of aquaculture. This study was based on the water quality data and meteorological data of online buoys SK11 and SK18 in Shuikou reservoir area of Minjiang River in Fujian from January to June, 2022. Then, back propagation(BP) neural network prediction model and MIC-BP neural network measurement model were used for machine learning, and the prediction results are given. At the same time, the prediction results of the two dissolved oxygen prediction models were compared and verified. The results showed that after the identification and screening of MIC (Maximum information coefficient), among the 13 input factors, the factors that had great correlation with dissolved oxygen include pH, water temperature, chlorophyll, electrical conductivity, turbidity, ammonia nitrogen concentration and nitrite nitrogen concentration. The effect of the mixed MIC-BP neural network model was obviously better than that of the independent BP neural network model. After the candidate factors were identified and screened by MIC, the performance of the model could be obviously improved. Compared with the independent BP neural network model, the results showed that the performance of MIC-BP neural network model at SK11 station decreased by 29.29%, RMSE decreased by 60.09%,and NSE increased by 27.63%,respectively. At station SK18, MAE decreased by 17.16%, RMSE decreased by 16.23%, and NSE increased by 12.77%,respectively.