To address the characteristics of complex backgrounds and multi-scale objects in remote sensing images, a remote sensing image object detection algorithm based on YOLOv8n, named PAM-YOLO, is proposed. First, in the backbone network, a triple pool hybrid attention (TPHA) mechanism is constructed and embedded. By fusing information from global maximum, average, and median pooling, it effectively suppresses the interference of background noise on channel features and enhances the feature representation of key regions. Second, a spatial context aware module (SCAM) is introduced in the neck structure. By mining global contextual information and building long-range dependencies between channel semantics and spatial structure, it improves the model′s ability to distinguish between targets and backgrounds. Finally, a parallel branch feature extraction (PBFE) module is designed and introduced. Through the deep decoupling and interactive fusion of three parallel branches for local details, global semantics and multi-scale context, it achieves a refined representation for objects of different scales. On the DIOR dataset, PAM-YOLO′s mAP50 increased by 1.8%, mAP50.95 by 2.4%, P-value by 0.3%, and R-value by 1.8%; its metrics on the DOTA dataset are also superior to other algorithms. The experimental results indicate that PAM-YOLO shows higher detection accuracy and robustness in the task of remote sensing image object detection.