The rotating target detection task in remote sensing image processing has the characteristics of wide-range scale variations，complex backgrounds and arbitrary target directions，which pose challenges to automatic target detection. In order to solve the above problems，this paper proposes a rotating target detection framework based on context space perception by using YOLOv5s detector. Firstly，a Context Space Perception Module (CSPM) is designed to construct a backbone network to obtain more comprehensive local context information and global space perception information，so as to solve the problem that the network model has insufficient feature extraction capability for multi-scale targets. Secondly，the non-parametric attention mechanism of SimAM is introduced into the feature fusion section，and the important information is adaptively fused based on the principle of neuron suppression to solve the problem of false detection and missed detection of the model in complex backgrounds. Finally，the angle parameter is added to perform direction regression of the rotating target，which solves the problem of target regression in any directions. Meanwhile，Gaussian Wasserstein Distance Loss (GWDL) is used to calculate the loss of the rotating frame. The parameters are jointly optimized to improve the detection accuracy. The Recall，Precision and mAP₅₀ of the proposed target detection algorithm on HRSC2016 dataset reach 0.955，0.916 and 0.904 respectively，which has the best detection effects. The algorithm also has a fine real-time performance with detection speed of 140.8 frames per second.