Because of the demand for intelligent detection of solid waste-based concrete three-dimensional (3D)-printed components in complex environments, this paper introduces the machine vision theory and proposes a target fine-segmentation algorithm based on the interlayer information entropy to realize the feature analysis and intelligent detection of 3D-printed components. First, considering the complex environment of concrete 3D printing, a preprocessing method for visual feature enhancement was constructed, the contrast was adjusted, and the image feature details were enhanced using Gamma grayscale transformation and histogram equalization algorithm. It was combined with adaptive median filtering to remove the random noise in images. Then, considering the layered superposition characteristics of the components, the interlayer information entropy index was defined, and a fine-segmentation method of printing components based on the interlayer information entropy and double threshold optimization was designed to realize the complex environment hierarchical detection and fine segmentation of 3D components. Finally, the target images of real concrete 3D-printed components were collected to verify the effectiveness of the proposed algorithm. Experimental results show that the proposed algorithm increases the accuracy by 12.44% and the F₁ value by 30.79% on average, considerably improving target segmentation accuracy. It lays the foundation for further realizing accurate measurement and path optimization of 3D-printed components.