<p>Manufacturers are striving to achieve higher energy efficiency without compromising production performance and quality standards. Parallel-serial structures, commonly found in modern production systems, offer a unique balance of flexibility and efficiency by combining parallel processes with sequential workflows. However, their inherent complexity poses significant challenges, particularly in optimizing energy efficiency and ensuring consistent product quality. In data-driven manufacturing environments, it is not clear how to leverage production data to enhance the energy efficiency of production systems. Therefore, this paper studied a data-driven approach to improving energy efficiency in parallel-serial production lines with product quality issues. Firstly, the authors developed a data-driven performance analysis method to evaluate the effects of disruption events, such as energy-saving control actions, machine breakdowns, and product quality failures, on system throughput and energy consumption. Secondly, a periodic energy-saving control method was developed to enhance system energy efficiency using a non-linear programming model. To reduce complexity and improve computational efficiency, the model was simplified by leveraging the intrinsic properties of parallel-serial production lines and solved using an adaptive genetic algorithm. Finally, the effectiveness of the proposed data-driven approach was validated through case studies, providing actionable insights into achieving data-driven energy efficiency optimization in complex production systems.</p>