餐饮包装消费者视觉偏好与情感反馈评测数据集

本数据集系统收录了不同年龄段、性别、地域及消费者画像群体对餐饮包装的视觉感知、情感反应与购买行为反馈信息，构建了以视觉吸引力、情绪共鸣与行为转化为核心的结构化数据体系。其字段包括用户ID、年龄段、性别、地域、消费者画像标签、包装ID、视觉吸引力评分、字体感知评分、插画/图案喜好、色彩搭配满意度、第一印象关键词、情绪共鸣标签、情感强度评分、购买意愿、是否加入购物车、主要选择该包装的前3原因、主要拒绝该包装的前3原因、视觉情感偏好类型。数据来源于标准化问卷调研与在线视觉测评系统，涵盖多种包装设计风格与视觉元素组合，确保样本分布在地域、性别与年龄等维度上的均衡性与代表性。通过统一的包装ID实现跨人群反馈的归一化处理，并结合多标签与评分数据，支持对不同消费群体视觉偏好特征与情感驱动因素的定量分析。该数据集可用于开展视觉元素对购买转化率的影响研究、区域化设计偏好分析、情感化营销策略优化、品牌形象感知诊断等任务，适用于餐饮品牌、食品包装设计公司、市场研究机构、电商平台等在包装设计优化与市场细分策略制定中的数据驱动决策。数据结构清晰、可扩展性强，适合持续更新与多场景应用。

This dataset systematically collects visual perception, affective responses and purchase behavior feedback information of different age groups, genders, regions and consumer profile groups towards food and beverage packaging, and constructs a structured data framework centered on visual attractiveness, emotional resonance and behavioral conversion. Its fields include user ID, age group, gender, region, consumer profile tag, packaging ID, visual attractiveness score, font perception score, illustration/pattern preference, color matching satisfaction, first impression keywords, emotional resonance label, emotional intensity score, purchase intention, whether added to cart, top 3 main reasons for choosing this packaging, top 3 main reasons for rejecting this packaging, and visual emotional preference type. The data is sourced from standardized questionnaire surveys and online visual evaluation systems, covering various packaging design styles and combinations of visual elements, ensuring a balanced and representative sample distribution across dimensions such as region, gender and age. Unified packaging IDs are used to normalize cross-group feedback, and combined with multi-label and rating data, it supports quantitative analysis of visual preference characteristics and emotional driving factors of different consumer groups. This dataset can be used for research on the impact of visual elements on purchase conversion rate, regional design preference analysis, emotional marketing strategy optimization, brand image perception diagnosis and other tasks, and is suitable for data-driven decision-making in packaging design optimization and market segmentation strategy formulation by food and beverage brands, food packaging design companies, market research institutions, e-commerce platforms and other entities. It features a clear data structure and strong scalability, making it suitable for continuous updates and multi-scenario applications.