In order to solve the problems of large size， low efficiency， and high deployment requirements for embedded devices of infrared multi-spectral ship targe detection models， a lightweight ship targe infrared multi-spectral detection model YOLOv8n-MFLW was proposed. Firstly， the model replaced the backbone network with a lightweight network， HGNetv2. Based on GSConv convolution， the modules of HGBlock and C2f were reconstructed to reduce the model parameter count while retaining the model's feature extraction and fusion capabilities. A self-adaptive sparse structured pruning algorithm， La-Depgraph， was proposed to prune the model， leading to a significant reduction in the model's parameters. Finally， an intermediate feature knowledge distillation learning strategy was employed to recover the accuracy loss caused by pruning and enhance the model's detection performance. Experimental results show that compared to the original model， the improved lightweight ship targe infrared multi-spectral fusion detection model achieves a detection accuracy of 96.4%， an increase of 1.2%. The model's parameter count， computational complexity， and memory usage are only 0.9 MB， 3.5 GFlops， and 2.3 MB， respectively， reduced by 88.1%， 81.2%， and 82.8%. Therefore， the proposed model is small in size and high in accuracy， it has a better detection performance and is capable for ship target detection tasks in complex environments.