Infrared target detection faces challenges such as limited effective pixels, low SNR, and difficulty in distinguishing targets from background and noise in the spatial domain. In response, we propose a infrared target detection method based on a spatiotemporal feature extraction module and an improved YOLOv5 object detection network. This method utilizes a three-dimensional residual structure to construct a space-time domain feature extraction module, enabling efficient extraction of space-time domain features of dim infrared targets and reducing interference from spatial domain noise in infrared image target detection. Additionally, we introduce the Coordinate Attention (CA) mechanism into the YOLOv5 convolutional neural network to address the challenge of detecting extremely weak targets relative to the background in weak target detection and improve the detection capability for weak targets. Experimental results demonstrate that compared to the YOLOv5s network, our proposed algorithm achieves a 2.2% increase in precision, a 2.1% improvement in recall, and a 3.5% increase in mean average precision at intersection over union 0.5. These results validate that the space-time domain feature fusion method can enhance the detection accuracy of weak infrared moving targets.