Consumer demand is the need for product characteristics expressed in their own words, which is the basis for producers to develop product recommendations. The extraction and analysis of consumer demands is the most critical input information in quality function deployment (QFD), which has a significant impact on the final prioritization of product technical features, product optimization, and subsequent configuration decisions in QFD, and is directly related to the success of product development. However, the traditional QFD approach to demand analysis lacks reliability and feasibility, and its application often requires time and labor costs that exceed the company’s actual capabilities. Therefore, this paper uses online reviews as the data source and constructs a latent Dirichlet allocation (LDA) topic model based on fuzzy sets to explore the consumer demand information reflected in user reviews. We also introduce the concept of word vector to improve the LDA topic model and compare it with the traditional topic model to verify the performance of the model, so as to explore the consumer demand behavior more accurately and efficiently.
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