To address the issues of missed and false detections in existing dense crowd pose estimation algorithms, an improved YOLOv8sPose algorithm for dense crowd pose estimation, namely, YOLOv8Pose-Dense Crowd (YOLOv8Pose-DC), is proposed. First, a centralized intrinsic adjustment feature pyramid network is designed, which combines deformable attention mechanisms and coordinate attention-based spatial pyramid pooling fast (CASPPF) in a parallel manner, globally focusing and adjusting the pyramid network from top to bottom, thereby increasing the spatial weight of global representation within the network. This enables the improved algorithm to obtain comprehensive and distinctive feature representations. Second, a multi-scale dual detection head structure is proposed, reducing computational complexity while enhancing model detection efficiency. Furthermore, the DySample module is utilized to improve the upsampling efficiency of the model. Finally, a spatial context aware module (SCAM) is added to enhance the model's ability in associating global information and suppressing irrelevant background features, to highlight human characteristics. Compared to the baseline model, YOLOv8Pose-DC increases mAP@0.5 by 3.1% and recall rate by 4.2%. The designed algorithm significantly improves performance and fully meets production requirements.