To address the issues of inaccurate target localization, missed detections, and false detections caused by large scale differences, diverse categories, and uneven distribution of targets in remote sensing images, an improved YOLOv8n remote sensing image target detection algorithm is proposed. Firstly, the SC_C2F module is constructed as the feature extraction module of the backbone network. By introducing spatial channel reconstruction convolution into the Bottlececk structure, the feature extraction ability of different scale channels and spaces is enhanced; Secondly, design an ESPPM module to replace the original pyramid pooling module, introduce an adaptive average pooling layer and a large separable kernel residual attention mechanism, enrich contextual information, and improve the model’s multi-scale feature aggregation ability; Again, by combining GSConv lightweight convolution with VoVGSCSP structure, the Slim PAN structure is introduced into the neck network to reduce model computation while maintaining detection accuracy; Finally, a rotation box with added parameter representation is introduced as the angle coordinate regression, and an RBCL loss function is designed to calculate the rotation box loss, making the detection process more in line with the target shape and improving the detection accuracy for small and dense targets. The improved YOLOV8n algorithm will be tested on the DOTA dataset and compared to the original algorithm mAP@0.5 Increase by 5.1% and reduce computational load by 0.4 GFLOPs.