Superpixels exhibit excellent image representation capability and high computational efficiency， which are widely applied in subsequent computer vision tasks. However， existing superpixel segmentation algorithms fail to fully integrate semantic and spatial information， resulting in insufficient spatial coherence and semantic consistency of the generated superpixels， as well as blurred and misclassified details when processing images with complex scenes. To address these problems， this paper proposed a Superpixel Segmentation algorithm based on the Inverted Feature Pyramid Network （SSIFPNet）. Through the cascaded coupling of an encoder and an Inverted Feature Pyramid Network （IFPN） decoder， the proposed algorithm better fused end-to-end multi-scale semantic information and spatial detail information. Meanwhile， a perceptual boundary-driven composite loss function was designed to further improve the semantic consistency of superpixels. Comparative experiments on the BSDS500， NYUv2， and KITTI datasets were conducted to compare SSIFPNet with state-of-the-art algorithms， including the Simple Linear Iterative Clustering superpixel segmentation algorithm （SLIC）， Superpixel Sampling Network （SSN）， and Superpixel segmentation algorithm with fully Convolutional neural Network （SCN）. Experimental results show that SSIFPNet achieves superior performance across all datasets. Specifically， the achievable segmentation accuracy （ASA） and Boundary Precision （BP） of SSIFPNet reach 0.972 0 and 0.130 6 on the BSDS500 dataset， 0.948 2 and 0.199 9 on the NYUv2 dataset， and 0.965 5 and 0.147 0， along with a COmpactness （CO） of 0.372 0， on the KITTI dataset. It is demonstrated that the superpixels generated by SSIFPNet possess favorable spatial coherence and semantic consistency and achieve superior performance in processing detailed texture regions， which fully demonstrates the superiority and generalizability of SSIFPNet.