Data and code underlying the publication: Color Strategies in AI Service Interfaces : Standardized Trust, Calculated Differentiation, and Design Isomorphism

This dataset and source code provide a comprehensive analysis of the color strategies employed by generative AI service interfaces. The study explores the visual trends of "Standardized Trust" and "Calculated Differentiation" within the AI industry through a quantitative lens.<strong>The repository includes:</strong><strong>Source Code-</strong>Python-based scripts for automated data collection (web crawling), color metadata extraction, and statistical analysis including Color Entropy and Principal Component Analysis (PCA).<strong>Dataset-</strong>Color data and metadata extracted from 82 representative generative AI services (selected from an initial pool of 100 global services).<strong>Analytical Outputs-</strong>Scripts to reproduce hue distribution graphs and PCA scatter plots as presented in the associated paper.This project offers empirical evidence of how AI services utilize specific color palettes to establish reliability and brand identity. It serves as a technical foundation for researchers in HCI (Human-Computer Interaction), UI/UX design, and AI-driven visual communication.

本数据集与配套源代码针对生成式AI（Generative AI）服务界面所采用的色彩策略展开全面分析。本研究以量化视角，深入探究了AI行业中「标准化信任」与「精准差异化」两大视觉趋势。
<strong>本仓库包含：</strong>
<strong>源代码：</strong>提供基于Python的自动化数据采集（网络爬虫）脚本、色彩元数据提取脚本，以及包含色彩熵（Color Entropy）计算与主成分分析（PCA）在内的统计分析脚本；
<strong>数据集：</strong>包含从初始100个全球AI服务池中筛选出的82家代表性生成式AI服务中提取的色彩数据与元数据；
<strong>分析产出物：</strong>提供可复现关联论文中色相分布图表与PCA散点图的脚本。
本项目为AI服务如何通过特定色彩调色板建立可信度与品牌身份提供了实证依据，同时可为人机交互（HCI，Human-Computer Interaction）、UI/UX设计以及AI驱动的视觉传播领域的研究者提供技术基础。