Aiming at the problem of reliable detection and accurate recognition of high dynamic aerial targets by infrared detectors carried by hypersonic vehicles in complex background, an aerial polymorphic target detection method based on deep spatial-temporal feature fusion was proposed. A weighted bidirectional cyclic feature pyramid structure was designed to extract the static features of polymorphic target, and switchable atrous convolution was introduced to increase the receptive field and reduce spatial information loss. For the extraction of temporal motion features, in order to suppress the complex background noise and concentrate the corner information into the moving region, the feature point matching method was used to generate the mask image, then the optical flow was calculated, and the sparse optical flow feature map was designed according to calculation results. Finally, the temporal features contained in multiple continuous frame images were extracted by 3D convolution to generate a 3D temporal motion feature map. By concatting the image static features and temporal motion features in channel dimension, the deep spatial-temporal fusion could be realized. A large number of comparative experiments showed that this method can significantly reduce the false recognition probability in complex background, and the target detection accuracy reached 89.87% with high real-time performance, which can meet the needs of infrared targets intelligent detection and recognition under high dynamic conditions.