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开展了全面剖析。本研究以量化视角，探讨了AI行业内「标准化信任」与「刻意差异化」两大视觉趋势。<strong>本仓库包含以下内容：</strong><strong>源代码：</strong>基于Python编写的脚本，可实现自动化数据采集（网络爬虫）、色彩元数据提取，以及色彩熵（Color Entropy）、主成分分析（Principal Component Analysis，PCA）等统计分析工作。<strong>数据集：</strong>从全球100个生成式AI服务的初始样本池中筛选出的82个代表性服务中提取的色彩数据与元数据。<strong>分析结果：</strong>可复现关联论文中展示的色调分布图表与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靠性与品牌辨识度提供了实证依据，同时可为计算机人机交互（Human-Computer Interaction，HCI）、UI/UX设计以及AI驱动视觉传播领域的研究者提供技术基础。