Commodity anti-counterfeiting decision in e-commerce trade based on machine learning and Internet of Things

Abstract

Some unscrupulous merchants saw the profitable sale of goods on the Internet. They took the opportunity to introduce fake and inferior goods and infringing goods online for sale. This practice greatly damaged the legitimate rights and interests of online shopping consumers. Therefore, how to effectively manage Internet fake sales is a big problem that needs to be solved urgently. This article mainly studies the decision-making of counterfeit goods in e-commerce trade based on machine learning and Internet of Things. This article defines the core issues of anti-counterfeiting of e-commerce goods, clarifies the purpose of anti-counterfeiting of e-commerce goods, innovates and proposes new ideas and methods of anti-counterfeiting systems from the perspective of machine learning and the Internet of Things Anti-counterfeiting sharing and anti-counterfeiting punishment, effectively deal with the issue of e-commerce goods purchase. The survey results of this paper show that about 89.9% of repurchasing users are in the 0.4-1 prediction score segment, indicating that the model's threshold setting at about 0.4 will have a good prediction effect, and the model's prediction effect is stable. The experimental results of this paper show that the prediction model in this paper can well predict users' purchasing behaviour and can shield and report merchants with fake and shoddy products.