At present, the identification of organic macerals in hydrocarbon source rocks is mainly done manually. It is not only affected by subjectivity in terms of accuracy but also has low efficiency. In order to achieve automatic identification of organic macerals in hydrocarbon source rocks and help understand the distribution and geochemical characteristics of organic matter in hydrocarbon source rocks, a lightweight source rock organic maceral identification algorithm hydrocarbon source rock-YOLOv8s (HSR-YOLOv8s) was proposed. The backbone network of YOLOv8s was replaced by ghost hourglass net version 2 (GHGNetv2) to reduce the number of parameters. The efficient multi-scale attention (EMA) was incorporated into the neck C2f structure and the C2f-EMA module was designed to make the network model pay more attention to context information and strengthen the extraction of features. Shape intersection over union (Shape-IoU) was quoted as the regression loss function of the bounding box to further improve the accuracy of bounding box prediction. Experiments show that the precision, mAP50, and mAP50:95 of the algorithm reach 90%, 88.4%, and 64.4% respectively, and the number of parameters is 8.36 MB. This algorithm not only has high detection accuracy but also has a lightweight network structure and is suitable for deployment in edge-end practice.