Manual construction tasks are physically demanding, requiring prolonged awkward postures that can cause pain and injury. Person posture recognition (PPR) is essential in postural ergonomic hazard assessment. This paper proposed an ergonomic posture recognition method using 3D view-invariant features from a single 2D camera that is non-intrusive and widely installed on construction sites. Based on the detected 2D skeletons, view-invariant relative 3D joint position (R3DJP) and joint angle are extracted as classification features by employing a multi-stage convolutional nerual network (CNN) architecture, so that the learned classifier is not sensitive to camera viewpoints. Three posture classifiers regarding arms, back, and legs are trained, so that they can be simultaneously classified in one video frame. The posture recognition accuracies of three body parts are 98.6%, 99.5%, 99.8%, respectively. For generalization ability, the relevant accuracies are 94.9%, 93.9%, 94.6%, respectively. Both the classification accuracy and generalization ability of the method outperform previous vision-based methods in construction. The proposed method enables reliable and accurate postural ergonomic assessment for improving construction workers' safety and healthy. •View-invariant 3D ergonomic postures recognition in single 2D camera•Simultaneous classification of arms, back, and legs postures in one video frame•View-invariant R3DJP and joint angle features extraction in single 2D image•Deep CNN-based learning is applied in 3D ergonomic postures recognition•The posture recognition accuracies of three body parts are 98.7%, 99.5%, 99.8%