Abstract:

[Purpose/Significance] Short video platforms have become the core arena for the digital presentation and dissemination of intangible cultural heritage (ICH). However, the "Matthew Effect" in the digital attention economy often causes high-quality ICH content to be submerged. Existing research predominantly suffers from "modal segmentation," focusing on single modalities such as text and visuals in isolation, which fails to explain how these elements synergistically drive user engagement. To address this gap, this study constructs a communication effect evaluation model based on multimodal machine learning. The innovation of this research lies in integrating computational communication methods with traditional persuasion theories, moving beyond simple content analysis to a quantifiable predictive framework. By identifying key influencing factors through data fusion, this study provides a scientific basis for optimizing the digital production strategies of the ICH content, offering significant value for enhancing the visibility of traditional culture and overcoming the barriers of digital dissemination. [Method/Process] This study integrates the elaboration likelihood model (ELM) and media ritual theory to establish a "cognitive-behavioral-cultural" dual-path analytical framework. Theoretically, the study maps content quality (video/audio/text) to the "Central Route" and source credibility (author attributes) to the "Peripheral Route." Empirically, focusing on ICH videos on Douyin as the subject, the study collected data from May 2024 to May 2025. After rigorous cleaning, a dataset of 2,869 valid samples was established. The study employs a multimodal feature engineering approach: visual and textual features are extracted to represent content quality; audio features (including FBank and MFCC) are processed using the OpenSMILE toolkit to capture prosodic and spectral characteristics; and author data are collected to quantify social influence. The Random Forest algorithm is utilized to fuse these heterogeneous data sources, analyze feature importance, and predict communication effectiveness. [Results/Conclusions] The empirical results demonstrate that the multimodal fusion model significantly outperforms single-modality approaches in predicting communication effects, confirming that ICH dissemination is a result of complex symbol interaction. Feature importance analysis reveals a distinct hierarchy: Author attributes make the highest contribution, indicating that the "Peripheral Route" - driven by the creator's social capital - is the decisive factor in determining communication heat. Its persuasive power far surpasses that of the content itself. Regarding content modalities, text and video follow in importance, serving as critical tools for user retention, while the audio modality holds supplementary semantic value by setting the emotional atmosphere. The study does not account for dynamic temporal changes or external trending events. Effective ICH dissemination requires a synergistic strategy: prioritizing the accumulation of the author's social influence as the core driver, while simultaneously optimizing visual and textual quality. Future research should incorporate time-series analysis to capture dynamic communication trends.

Key words: information diffusion, Sora, short video communication effect, intangible cultural heritage (ICH), multimodal data, feature engineering