The aluminum electrolysis production environment is harsh, influenced by the coupling of multiple physical fields such as electric, magnetic, flow, and temperature fields, leading to frequent failures during the production process. The temperature of the aluminum electrolysis cell is a crucial parameter that affects the lifespan and operational status of the electrolysis tank. However, due to the high temperatures and corrosive nature of the tank, no effective online detection or prediction method for electrolysis temperature has been established so far. To address this issue, this study reveals the close correlation between the electrolysis temperature of aluminum electrolyzers and their process parameters through theoretical analysis and on-site experimental validation. Based on this, a deep learning-based model for predicting the electrolysis temperature is proposed. Considering the complexity, nonlinearity, high dimensionality, and temporal sequence of the process parameters, Convolutional Neural Networks (CNN) are employed to extract highdimensional features from the data, while Long Short-Term Memory (LSTM) networks are used for modeling. Additionally, the Attention mechanism is introduced to capture the relationships between different parts of the input parameters and to weigh the data according to its importance. A PID-based Search Algorithm ( PSA) is applied to optimize the CNN-Attention model for the aluminum electrolysis process, reducing training time and improving model performance. Experimental results demonstrate that the proposed temperature prediction model achieves a correlation index (R 2 ) of 0. 963 7, with a Root Mean Square Error (RMSE) of 5. 417 6 and a Mean Absolute Error (MAE) of 3. 382 5. A comparison with single-model algorithms, other prediction models, and different optimization techniques shows that the proposed model significantly outperforms them. The model successfully predicts the electrolysis temperature of the aluminum electrolyzer, enabling real-time, online detection of the electrolysis temperature during production.