CO2 capture is one of the most promising strategies to combat the increasing CO2 concentration in the atmosphere, but the high energy requirement has prevented its widespread use. A new latent heat reuse system based on pressure swing technology was proposed for the post-combustion CO2 capture (PCC) process, which can recover more than 50% of the energy from compressed flue gas. As a representative of deep eutectic solvents (DES), reline was adopted as an absorbent for this PCC process and was shown to be suitable for flue gas decarbonization. An artificial neural network (ANN) model was then trained to explore the relationship between the operating parameters of the designed PCC process and their capture results. The ANN model can provide direction for multi-parameters selection as it can quickly predict and find combinations of operational parameters that meet the capture objectives. The result of this work shows a 68.8 % reduction in total capture energy requirement after improvement (1.28 MJ/kg CO2) compared to the conventional MEA-based capture process (4.1 MJ/kg CO2).