In this letter, we propose a semantic guided infrared and visible image fusion method, which can train a network to fuse different semantic objects with different fusion weights according to their own characteristics. First, we design the appropriate fusion weights for each semantic object instead of the whole image. Second, we employ the semantic segmentation technology to obtain the semantic region of each object, and generate special weight maps for the infrared and visible image via pre-designed fusion weights. Third, we feed the weight maps into the loss function to guide the image fusion process. The trained fusion network can generate fused images with better visual effect and more comprehensive scene representation. Moreover, we can enhance the modal features of various semantic objects, benefiting subsequent tasks and applications. Experiment results demonstrate that our method outperforms the state-of-the-art in terms of both visual effect and quantitative metrics.