A lightweight Human Pose Estimation （HPE） network based on redundant feature suppression was proposed to address the difficulty of balancing computational efficiency and localization accuracy of the existing HPE networks in complex scenarios. It was named LE-SHNet （Lightweight Enhanced Stacked Hourglass Network）. Firstly， the Multiple Separated Hourglass Module （MSHM） was designed to employ heterogeneous convolution branches for differential modeling of the features of large joints and distal limbs， while suppressing redundant computations. Then， the Shuffle Efficient Channel Attention （SECA） was integrated between MSHMs， so as to combine channel shuffling and adaptive kernel convolution to enhance hierarchical joint correlations with zero additional parameters. Finally， the Spatial and Channel Perception Module （SCPM） was constructed in non-MSHMs to strengthen perception ability of key areas by spatial-channel reconstruction and Triplet Attention （TA） mechanism. Experimental results show that LE-SHNet achieves Average Precision （AP） of 88.7% on MPII （Max Planck Institute for Informatics） and 71.3% on COCO2017 （Common Objects in COntext 2017）， while reduces the number of parameters by 49.3%， reduces the computational cost by 28.2%， and increases the Average Precision （AP） by 1.0 percentage points compared with the baseline network — Two Stacked Hourglass Network （2-SHNet）； compared with the lightweight HPE networks EL-HRNet （Efficient and Lightweight High-Resolution Network） and MobileMultiPose （Mobile-friendly and Multi-feature aggregation Pose estimation）， LE-SHNet achieves AP improvements of 1.0 and 0.8 percentage points， respectively， while reducing the number of parameters by 32.0% and 26.7%， respectively. It can be seen that LE-SHNet maintains lightweight properties while improving keypoint localization accuracy， so that it has potential application values for real-time deployment on edge devices in scenarios such as intelligent monitoring， human-computer interaction， and sports rehabilitation.