In real-world scenarios， human images are often occluded by clothing， self-posture， and environmental objects， leading to insufficient visible information， so that the existing human reconstruction methods tend to degrade to mean models in shape modelling， failing to recover real individual characteristics faithfully. To address this issue， a Human Dimension Attention Regressor （HDAR） method for monocular occluded human mesh recovery was proposed. Firstly， human dimensions in the visible region were used to infer the dimensions of occluded parts. Secondly， a hierarchical proportion constraint was introduced， in which first-level constraints were applied to adjacent body parts and second-level constraints were applied to distant body parts， thereby ensuring that the regressed shapes conform to human structural characteristics. Finally， Two-Dimensional （2D） joint information was integrated with the body dimension information for iterative optimization， so as to improve pose estimation accuracy. Experimental results on the 3DPW （Three-Dimensional （3D） Poses in the Wild） dataset show that， the proposed method achieves a Per Vertex Error （PVE） of 65.2 mm， which is 10.7 mm lower than that of Multi-HMR （Multi-person whole-body Human Mesh Recovery） under occlusion conditions， corresponding to a 14.1% error reduction. Visualization experimental results demonstrate that the proposed method improves the reconstruction accuracy of human shape and pose in complex occlusion scenarios effectively.