Aiming at the problems of long segmentation time and inaccurate segmentation of small targets in remote sensing image semantic segmentation, a fast semantic segmentation model of high-resolution remote sensing image based on multi-level feature cascade network (MFCNet) was proposed. The model was mainly composed of feature encoding, feature fusion and target refinement. Feature encoding extracted the input images feature of different resolutions and used different backbone networks. Due to the lower resolution of low-resolution images, heavy-weight backbone networks were used to obtain rich semantic information with fewer parameters. For medium and high-resolution images, lightweight backbone network was used to reduce the amount of parameters and obtain global information. While medium and low-resolution encoding used the way of weights and calculation sharing to further reduce model parameters and computational complexity. The feature fusion section fused features from different branches to obtain information at different scales. The target refinement used residual to correction the fused features and the features of the coded part to restore the spatial detail information of the image, making the segmentation more accurate. And the entire model worked efficiently in an end-to-end manner. The experimental verified the validity of the model in semantic segmentation of remote sensing images, and achieved a good balance between model complexity and accuracy.