Abstract:In the era of big data, how to grasp customer needs through data analysis and increase the scientific nature of product optimization is of strategic importance to enterprises. This study applies online comment data to the assisted optimization of enterprise products, proposes techniques and methods for obtaining product optimization information, and realizes the acquisition of product optimization information. Firstly, we calculate the indicators such as customer attention and satisfaction in online reviews, and construct a weighting algorithm model for customer opinions. Next, the word pairs of product characteristics and customer opinions are extracted, and the weight of customer opinions is calculated according to the weight algorithm model. Then, the corresponding product optimization information is found through the correlation matrix. Finally, the feasibility of the method is verified by an example.