Based on the detection data of the two farms in Fengxian District of Shanghai from 2014 to 2018 and in 2021,8 water quality indicators,including the water temperature (T), dissolved oxygen (DO), permanganate index(I_Mn), total phosphorus (TP), total nitrogen (TN),ammonia nitrogen (TAN), nitrite nitrogen (NO₂^--N) and nitrate nitrogen (NO₃^--N) were chosen to establish a prediction model based on principal component analysis (PCA) and long short-term memory (LSTM). Firstly, through principal component analysis which was used to reduce data feature extraction and dimension, I_Mn and TAN were determined to be the water quality prediction indexes to build a LSTM model based on the PCA analysis,then the PCA-LSTM model was used to predict the water quality of different ponds;Finally,comparison was carried out with a single LSTM model to verify the strengths and weaknesses of both models. The results show that the PCA-LSTM model can be used to predict I_Mn and TAN in Litopenaeus vannamei aquaculture ponds,and the prediction results are better than the single LSTM model.