Research on Graphic 3D Modeling Technology Based on Neural Network in Intelligent Construction System

This paper mainly discusses the innovative application of neural network in 3D graphic modeling and designs an efficient 3D modeling algorithm. The algorithm takes deep learning as the core and realizes high-precision modeling of building structures by extracting multi-level features of graphic data. CNN is used to process multi-dimensional images to generate 3D models with rich details, which greatly improves the modeling efficiency and accuracy. In addition, this paper introduces the simulation technology of the model in the intelligent construction system, and verifies the stability and robustness of the algorithm through experiments under different parameters. The experimental results show that the proposed modeling algorithm achieves higher accuracy than traditional algorithms in complex structure recognition and saves nearly <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{3 0 \%}$</tex> in computing time. Data analysis further confirms the adaptability and effectiveness of the neural network model in complex building environments, and provides data support and algorithm basis for in-depth research on future intelligent construction systems.