摘要： Organic solar cells (OSCs) based on ternary blend active layers are among the most promising photovoltaic technologies. To further improve the power conversion efficiency (PCE), the materials selection criteria must be focused on achieving high open-circuit voltage (Voc) through the alignment of the energy levels of the ternary blend active layers. Hence, machine-learning approaches are in high demand for extracting the complex correlation between Voc and the energy levels of the ternary blend active layers, which are crucial to facilitate device design. In the present work, the data-driven strategies are used to generate a model based on the available experimental data and the Voc are then predicted using available machine-learning methods (Random Forest regression and Support Vector regression). In addition, the Random Forest regression is compared with Support Vector regression to demonstrate the superiority of Random Forest regression for Voc prediction. The Random Forest regression is then developed to find the appropriate energy level alignment of ternary OSCs and to reveal the relationship between Voc and electronic features. Finally, an analysis based on the ranking of variables in terms of importance by the Random Forest model is performed to identify the key feature governing the Voc and the performance of ternary OSCs. From the perspective of device design, the machine-learning approach provides sufficient insights to improve the VOC and advances the comprehensive understanding of ternary OSCs.