Innovative assisted design of accessible products based on multi-dimensional perceptual information association rule extraction

With limited data and information available in the early stages of accessibility product development, it is difficult for product developers to conduct all the specific experiments to determine the technical performance due to time and cost constraints. In this paper, after completing the required screening of Chinese natural evaluation language, we obtain the vocabulary base for quantitative model construction. And the construction of the semantic quantitative model is carried out according to the characteristics of the vocabulary problem related to product perception imagery in the study. By combining the association rule mining algorithm, the fuzzy weighted support is calculated, so as to obtain a more reasonable and effective final priority ranking of TCs. After the screening of imagery word pairs, Python language is utilized to realize the association of user requirements and output the results. The final assessment of the overall accessible product development and design, 8 for the subjects to evaluate the results are all A grade or A +. This study can provide effective and reasonable guidance for accessible product development and design decision-making, and ensure reliability in user requirement analysis.