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

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.