[目的/意义]互联网环境下，移动社交媒体中的图像文字等多模态用户生成内容（UGC）已成为影响用户决
策的重要信息类型。关于其用户信息采纳的研究大多将图文等多模态内容割裂看待，导致UGC的多维特征，尤
其是融合特征，对用户信息采纳的综合影响机制尚不清晰。[方法/过程]文章选取小红书这一代表性的经验分享
型移动社交媒体作为研究对象，利用自然语言处理与机器视觉技术对信息分析方法进行改进，提出覆盖图文深
层、浅层以及融合特征的多模态UGC多维特征测度方法，并进一步构建多维特征对用户信息采纳的影响机制计
量模型。[结果/结论]研究发现，两种图文协调性特征对于用户信息采纳的影响力显著高于单独的图像或文字特
征；图文浅层特征比深层特征的影响力更大；影响用户信息采纳的各关键特征之间存在调节作用；自然、物体和其
他这三类场景中的具体影响机制存在异质性。研究对于促进移动社交媒体中高质量的UGC创作和有效的信息
采纳具有重要参考价值。
[Purpose/significance] In the Internet landscape, multimodal User-Generated Content (UGC) comprising
images and text on mobile social media has emerged as a crucial information source influencing users' decision-mak⁃
ing. However, existing studies on user information adoption have analyzed multimodal content such as images and text
in a piecemeal fashion, leading to a lack of clarity regarding the comprehensive influence mechanism of multidimen⁃
sional UGC features, especially fusion features, on user information adoption. [Method/process] This study employs
Xiaohongshu, a representative experience-sharing mobile social media platform, as the research object. We further im⁃
prove the information analysis method by utilizing NLP and CV technologies. Specifically, we propose a multidimen⁃
sional feature measurement method for multimodal UGC, which captures both images and text's deep, shallow, and fu⁃
sion features. Furthermore, we develop an econometric model to investigate the influence mechanism of multidimen⁃
sional features on user information adoption. [Result/conclusion] The results indicate that the impact of two image-tex⁃
tharmony features on user information adoption is significantly higher than that of image or text features in isolation.Ad⁃
ditionally, the influence of shallow features is greater than that of deep features; the key features affecting user informa⁃
tion adoption exhibit moderating effects; and the specific influence mechanisms differ in the three types of scenarios:
nature, object, and others. The findings of this study provide crucial insights for facilitating high-quality UGC creation
and effective information adoption in mobile social media.
