User Review Data-Driven Product Optimization Design Method

Aiming at the problems of time-consuming,labor-intensive and low efficiency in traditional product design optimization,a data-driven product optimization design method is proposed.The online reviews are crawled based on Scrapy.According to the characteristics of the text data,the K-means algorithm is used to analyze user needs,and the optimization target is achieved on the basis of the clustering results.The feature coding is performed on the optimization target,and the product feature optimization iteration is implemented based on non-dominated sorting genetic algorithm-Ⅱ(NSGA-Ⅱ)to obtain the final result.Taking a certain brand of rice cooker as an example,the optimum proposal is compared with the initial samples by the evaluation index of customer satisfaction to verify the effectiveness of the proposed method.