Currently, the depletion of resources has become a serious problem worldwide. In order to promote sustainable development, people are encouraged to minimize the waste from their products, while companies should produce remanufactured products such as second-hand laptops and smartphones. On the Electric Commerce (EC) platform, people engage in trading the second-hand products among themselves and with businesses. The expansion of the second-hand product market on EC platforms contributes to reducing the amount of product waste. On EC platforms, individuals or groups acting as online retailers list second-hand products. However, these second-hand products often fail to accurately reflect online shopper demand. This study proposes a prediction model for the selling status of second-hand products on the EC platform based on morphological analysis and classification results. First, morphological analysis is performed on online shopper comments for second-hand products to reveal frequently used noun words. Second, the features of these frequently used words are calculated using principal component analysis, and online shoppers are classified into clusters. Finally, a prediction model for the status of second-hand products is proposed based on the cluster information.