Studies indicate that the continental shale oil reservoirs in China exhibit complex lithology and well-developed laminae. Laminae with dominant lithology are found to create ample reservoir space and migration pathways for oil and gas. Moreover, the laminae plane acts as a beneficial natural weak surface during reservoir fracturing stimulation. Hence, investigating the synergy of “laminae + favorable lithology” is crucial for evaluating the “sweet spots” in shale oil exploration. In this paper, a method for dominant lithofacies identification based on “macrostructure + lithology” is implemented by utilizing image processing and threshold segmentation technology on electrical imaging logging data. Specifically, the method comprises three main steps: (i) Precisely characterizing laminae through “Hough Transform” line detection image processing based on vertical slicing of electrical imaging logging data; (ii) Utilizing experimentally analyzed lithology as a scale for electrical imaging logging, and applying threshold segmentation technology for quantitative lithology identification; (iii) Developing a lithofacies classification scheme that combines shale oil lithology and macrostructure for quantitative identification of dominant lithofacies. The laminae identification results in this study exhibit a high coincidence rate with geological characteristics, effectively bridging the gap between geological characteristics and logging data and offering a crucial reference for geological evaluation. The method played an important role in the evaluation of laminar shale oil for Gulong Area in Daqing Oilfield and Longdong Area in Changqing Oilfield, aiding the optimization of sweet spot intervals. It holds considerable potential for further application and promotion.