Dissolved Oxygen(DO) is a key index of the water environment. Accurate prediction of time series of DO concentrations is greatly important for water environment management. A novel DO concentrations prediction model based on LSTM method with improved weights dual-stage Attention mechanism (DAIW-LSTM Model) was proposed, and was applied to predict the daily DO concentrations in Liuxi River Basin. The model uses spatial attention in encoder and temporal attention in decoder, the encoder and decoder each contain a new mechanism of weight optimization in two stages. Taking the advantages of the new model in predicting time series data with stronger nonlinearity and more prominent non-stationarity, a comparative analysis among different baseline models for three water quality monitoring stations was carried out, and the upstream feature variables input and the effects of feature weight optimization mechanism were discussed. Results showed that: (1) Comparing with baseline models such as DA-LSTM, LSTM, Bi-LSTM, the applicability and accuracy of the model were verified; The SMAPE, MAE and MSE predicted by DAIW-LSTM model at Baiyunlixiba station were 0.075, 0.611 and 0.712 respectively, which were the best of all models, and could be reduced by 9.61%, 8.06% and 18.17% respectively compared with DA-LSTM model. (2) Further tests with the input of upstream characteristics showed that the prediction effect of the new model was further improved, it also proved the importance of selecting upstream feature variables. The attention mechanism could effectively solve the problem of insufficient robustness of the traditional LSTM model with additional noise. (3) The second stage could optimize and correct the initial weight of the first stage in the new attention weight optimization mechanism. The prediction accuracy of the new model would be improved, since the important features such as pH, conductivity, water temperature, and air temperature, were automatically strengthened. The research results could be used as a good reference for the research on water quality predictions.