UAV aerial images have the characteristics of complex background, small and dense targets. Aiming at the problems of low precision and a large number of model parameters in UAV aerial image detection, an efficient multi-scale feature transfer small target detection algorithm based on hypergraph computation is proposed. Firstly, a multi-scale feature pyramid network is designed as a neck network to effectively reduce the problem of information loss caused by lengthy transmission paths by fusing multi-layer features in the middle layer and transmitting them directly to adjacent layers. In addition, the feature fusion process uses hypergraphs to model higher-order features, improving the nonlinear representation ability of the model. Secondly, a lightweight dynamic task-guided detection head is designed to effectively solve the problem of inaccurate detection targets caused by inconsistent classification and positioning task space in the traditional decoupling head with a small number of parameters through sharing mechanism. Finally, the pruning lightweight model based on layer adaptive amplitude is used to further reduce the model volume. The experimental results show that this algorithm has better performance than other architectures on VisDrone2019 dataset, with the accuracy mAP_0.5 and parameter number reaching 42.4% and 4.8 M, respectively. Compared with the benchmark YOLOv8, the parameter number is reduced by 54.7%. The model achieves a good balance between detection performance and resource consumption.