For the difficulty in extracting deep features of batch process variables, as well as low quality prediction accuracy caused by the temporal, nonlinear, and dynamic characteristics of variables, this article proposes a quality prediction model for batch processes based on convolutional neural networks spatial and temporal attention with double long short term memory networks (CNN-STA-DLSTM). Firstly, the three-dimensional data of the batch process are expanded into a two-dimensional matrix along the direction of the variables, and the two-dimensional data are normalized by the Max-Min method. Then, the partial least squares (PLS) method is used to reduce the dimension of the original data, and the variables with strong correlation with the quality variables are retained. The convolutional neural network (CNN) is used to mine the potential features of the process data and improve the attention of the quality-related feature information. Secondly, the temporal attention mechanism and the spatial attention mechanism are introduced to construct the encoder-decoder structure network of the double-layer LSTM, and the attention mechanism is used to adaptively learn the relevant historical information of the time step, so as to improve the long-term memory ability of the model and strengthen the spatio-temporal correlation between the process variables and the quality variables. Then, the random-grid search method is used to optimize the hyperparameters of the prediction model, and the prediction model is constructed. Finally, the penicillin fermentation simulation platform and the hot strip rolling production process data are used for experimental verification. The results show that the proposed model has more accurate prediction effect.