To address the challenges of strong coupling of wastewater treatment processes, data nonlinearity and high volatility, real-time regulation is achieved by predicting the aeration amount, thus reducing the aeration energy consumption in the biochemical section. Based on this, hybrid model termed CLT (CEEMDAN-LSTM-Transformer) was proposed to achieve precise prediction of aeration volume. This model integrates CEEMDAN (complete ensemble empirical mode decomposition with adaptive noise), LSTM(long short-term memory) networks, and the Transformer architecture. First, the aeration volume was decomposed using CEEMDAN to generate new components. These components were combined with the original variables and input into LSTM to capture deep temporal information. Then, the Transformer model was applied for parallel data processing, focusing on all parts of the sequence to improve understanding of long-distance dependencies. Finally, ablation, comparative, and generalization experiments were conducted using actual process data. The coefficient of determination (R²) of the CLT model reaches 99%, and the coefficient of determination in the generalization test is 98%. Superior generalization abilities are demonstrated. The model effectively achieves precise prediction of aeration volume, providing methods and references for reducing aeration energy consumption.