Research and Implementation of Abnormal Product Identification Model Based on Stability

Abstract

With the development of the economy, online shopping has gained widespread popularity in all aspects. Due to its advantages such as convenience, speed, time and effort saving, and door-to-door delivery, it is increasingly favored by people and has become an indispensable part of daily life. With the improvement of people’s economic ability and consumption level, the demand for on-line shopping experience is also increasing. At the same time, competition among major online retail businesses has become increasingly fierce. In order to attract consumers’ attention and increase product sales, some businesses have started to use “speculation” and “order” methods such as selling, positive reviews, and negative reviews to maliciously promote products, in-fringing on consumers’ rights and interests. To protect consumers’ right to know and choose, this project uses a dataset provided by Inspur Zhuosu Company to analyze the reasons for abnormal products through a combination of quantitative and qualitative data mining analysis. Mathematical modeling and machine learning methods are used to define some abnormal product indicators, and these indicators are used to construct a model for finding and predicting abnormal products. The experimental results indicate that the model has good performance and certain practicality.