Digital twin intelligent modeling design and cross-platform collaborative dissemination mechanism of intangible heritage elements

The current digital modeling and cross-platform dissemination of intangible cultural heritage elements face the challenges of low efficiency and accuracy. Existing methods exhibit significant limitations in terms of model fidelity, terminal adaptability, and the effectiveness of feedback loops. In this study, an intelligent modeling and cross-platform collaborative dissemination mechanism based on digital twins (DTs) is proposed to achieve high-fidelity reconstruction, lightweight optimization, and dynamic iteration. An initial DT model is constructed by integrating high-precision 3D scanning and computer vision technologies. A lightweight convolutional neural network, combined with a joint geometry−texture loss function, is designed to automatically inpaint model details and optimize quality. The GL transmission format encapsulation and an API gateway are used to establish data transmission channels, enabling seamless integration of mobile devices, virtual museums, and social media platforms. A personalized recommendation algorithm driven by a graph neural network is introduced, which integrates user behavior data to deliver precise content recommendations. A real-time feedback mechanism based on Kafka and Spark Streaming is constructed to transmit dissemination effect data back to the modeling and recommendation modules, forming a dynamic optimization loop. The experimental results show that this method reduces the Hausdorff distance of the paper-cutting intangible cultural heritage model from 0.858 to 0.631 mm and reduces the cross-platform synchronization delay on entry-level devices from 987 to 512 ms. The proposed method provides a scalable technical path for the intelligent inheritance and global dissemination of intangible cultural heritage.