Remote sensing object detection usually faces challenges such as large variations in image scale, small and densely arranged targets, and high aspect ratios, which make it difficult to achieve high-precision oriented object detection. This study proposes a global context attentional feature fusion pyramid network. First, a triple attentional feature fusion module is designed, which can better fuse features with semantic and scale inconsistencies. Then, an intra-layer conditioning method is introduced to improve the module and a global context enhancement network is proposed, which refines deep features containing high-level semantic information to improve the characterization ability. On this basis, a global context attentional feature fusion pyramid network is designed with the idea of global centralized regulation to modulate shallow multi-scale features by using attention-modulated features. Experiments have been conducted on multiple public data sets, and results show that the high-precision evaluation indicators of the proposed network are better than those of the current advanced models.