Scholarlink

<p>The current product evaluation systems, constrained by fixed indicators and subjective weighting, struggle to adapt to dynamic market demands. This study proposes a data-driven framework integrating latent Dirichlet allocation, fuzzy analytic hierarchy process, and particle swarm optimization. User reviews and product parameters are collected from e-commerce platforms, with latent Dirichlet allocation extracting latent themes to build a multi-dimensional indicator system. The fuzzy analytic hierarchy process quantifies qualitative indicators to form an initial judgment matrix, while particle swarm optimization performs global optimization and consistency correction to minimize expert bias. An empirical case on smartwatches verifies the feasibility and effectiveness of the proposed framework, demonstrating its ability to capture user-centered demand characteristics and produce comprehensive evaluation results. The proposed evaluation system effectively combines demand insights with market adaptability, offering a robust theoretical and methodological foundation for product design optimization and differentiated strategy formulation.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A hybrid intelligent model that performs product evaluation via semantic mining and optimized decision processing

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月15日

查看全文
  • Dezheng Wu,
  • Xueqian Jiang,
  • Yawen Hu,
  • Kexiang Li

摘要

The current product evaluation systems, constrained by fixed indicators and subjective weighting, struggle to adapt to dynamic market demands. This study proposes a data-driven framework integrating latent Dirichlet allocation, fuzzy analytic hierarchy process, and particle swarm optimization. User reviews and product parameters are collected from e-commerce platforms, with latent Dirichlet allocation extracting latent themes to build a multi-dimensional indicator system. The fuzzy analytic hierarchy process quantifies qualitative indicators to form an initial judgment matrix, while particle swarm optimization performs global optimization and consistency correction to minimize expert bias. An empirical case on smartwatches verifies the feasibility and effectiveness of the proposed framework, demonstrating its ability to capture user-centered demand characteristics and produce comprehensive evaluation results. The proposed evaluation system effectively combines demand insights with market adaptability, offering a robust theoretical and methodological foundation for product design optimization and differentiated strategy formulation.