疫情下电商数据对产品设计决策的影响研究

Influence of E-commerce Data on Product Design Decision in Epidemic Situation

中文关键词: 疫情  爬虫技术  产品设计  数据处理  用户评论  产品图像

英文关键词: epidemic  crawler technology  product design  data processing  user reviews  product images

中文摘要:

      目的 在当前疫情影响的环境下，提出产品设计与电商行业大数据深度融合的方法。方法 首先，通过产品类型寻找合适的数据源，应用Python自然语言编写爬虫从数据源爬取两类数据，一类是以文本类资源为主的用户评论，另一类是以图像类资源为主的产品主视图。然后，使用文本聚类分析和机器学习的方法对两类数据进行批量化处理，生成有效数据，并对有效数据进行图表可视化。最终，综合分析图表，从而有效的指导设计团队进行设计工作。结论 通过大数据分析，精准挖掘疫情背景下产品的现有发展趋势，重新定义用户在疫情背景下对现有产品使用的心理反馈。这种设计方法使产品设计方案打破设计师个人认知所带来的局限性，使方案更加符合当前疫情下用户的消费心理，顺应市场的发展。以儿童学步鞋为例，证明了该方法的有效性。

英文摘要:

      The paper aims to promote the application of deep integration of product design and big data in the e-commerce industry under the influence of the current epidemic. First, find a suitable data source by product type, and use Python natural language to compile a crawler to crawl two types of data from the data source:user comments based on text resources and product main views based on image resources. Then perform batch processing on the two types of data by text clustering analysis and machine learning methods to generate valid data, and visualize the valid data in charts. Finally, comprehensively analyze the charts to provide effective guidance to the design team. Through big data analysis, we can accurately explore the current development trends of products under the background of the epidemic, redefine the psychological feedback of users to the use of existing products under the background of the epidemic, and make the product design plan break the limitations of the designer's personal cognition, which is more in line with the consumer psychology of users and the development of the market under the current epidemic. Taking children's toddler shoes as an example, this paper proves the effectiveness of the method above.