In order to improve the existing time series algorithms for precipitation forecasting, this paper proposes a time series precipitation forecasting model (DeepAMogLSTM) based on improved dual-stage attention mechanism.The algorithm can be divided into two parts.In the input attention stage, a three-layer attention mechanism is designed to pay multiple attention to the input sequence; while in the time attention stage, the hidden state most relevant to the target value is selected to calculate the long-term correlation of the time sequence.In this manner, input features can be stably selected and input into the prediction structure.The algorithm also introduces Mogrifier LSTM (Long Short-Term Memory) to enhance the feature representation ability.The model uses preprocessed automatic station data from 2016 to 2019 and ECMWF weather field model data for integrated forecast, and corrects the model forecasts using observation data of the same period.The experimental results show that the evaluation indexes of the model are improved in the 2-hour precipitation nowcasting, in which the maximum square root error is 1.877 mm, the maximum average absolute error is 0.727 mm, and the goodness of fit (R²) is 0.783.At the same time, the modeled precipitation fits actual precipitation in spatial distribution, which is better than the correction effect of other models.