In view of the problems of PVC moisture content in the PVC drying section, such as nonlinearity, large lag, complex correlation with other variables and difficult to predict, a multiple attention mechanism and weight correction long-term and short-term memory network (LSTM) model are proposed for the prediction of PVC moisture content. In the encoder part: use the correlation between the input sequences related to water content to correct the variable weight of spatial attention mechanism training, so as to avoid the large weight difference between the input variables with strong correlation due to simple data training, and then the actual drying process is inconsistent. At the same time, due to the hysteresis of water content prediction, in order to reduce the loss of cell state information of LSTM unit in the eldest son time window, an information compensation mechanism is proposed to compensate the cell state information at the previous time. In the decoder part, we use the time attention mechanism to update the weight of the hidden layer state of the encoder, and remove the limitation of the fixed length vector on the performance of the model. Finally, the DCS data of the drying section of a chemical company were selected for verification. Compared with RNN, VA-LSTM and STA-LSTM, the correlation coefficient (R2) were increased by 571%, 122.6% and 82.6% respectively. The results showed that the model in this paper had certain advantages.