The significant size difference of targets and the complex background in remote sensing images may lead to false detection and missed detection. Meanwhile，existing algorithms have problems of large parameter number and high computational cost. Therefore，a lightweight remote sensing target detection algorithm based on YOLOv8n is proposed. Firstly，the C2f module of the neck is replaced with the CSPStage module to enhance the learning of features in different feature layers by introducing the integration mechanism of gradient change. Secondly，CG module is introduced to reconstruct a Bottleneck module in C2f，and the feature processing ability of the network is enhanced by combining context information. Then，based on PConv，lightweight detection header PDetect is designed to reduce the waste of redundant information on computing resources. Finally，the Focaler-Shape-IoU loss function is designed to make the model focus on the shape and scale factors of the border itself，make up for the influence of sample difficulty distribution on the border regression，and improve the convergence speed and generalization performance of the model. The experimental results show that the mAP value obtained by the improved network model on the open remote sensing dataset NWPU VHR-10 is 4.1 percentage points higher than that obtained by the original YOLOv8n algorithm，the parameters is reduced by 37%，and the FLOPs is reduced by 45%，which proves the effectiveness and advanced nature of the improved algorithm.