Social e-commerce has been a hot topic in recent years, with the number of users increasing year by year and the transaction money exploding. Unlike traditional e-commerce, the main activities of social e-commerce are on social network apps. To classify sellers by the merchandise, this article designs and implements a social network seller classification scheme. We develop an app, which runs on the mobile phones of the sellers and provides the operating environment and automated assistance capabilities of social network applications. The app can collect social information published by the sellers during the assistance process, uploads to the server to perform model training on the data. We collect 38,970 sellers’ information, extract the text information in the picture with the help of OCR, and establish a deep learning model based on BERT to classify the merchandise of sellers. In the final experiment, we achieve an accuracy of more than 90%, which shows that the model can accurately classify sellers on a social network.

近年来，社交电子商务已成为热门话题，用户数量逐年增加，交易金额激增。与传统电子商务不同，社交电子商务的主要活动是在社交网络应用程序上。为了按商品对卖家进行分类，本文设计并实现了社交网络卖家分类方案。我们开发了一个应用程序，该程序可在卖方的手机上运行，​​并提供社交网络应用程序的操作环境和自动协助功能。该应用程序可以收集卖家在协助过程中发布的社交信息，并将其上传到服务器以对数据进行模型训练。我们收集了38,970个卖家信息，借助OCR提取了图片中的文字信息，并建立基于BERT的深度学习模型对卖家的商品进行分类。在最终实验中，我们达到了90％以上的准确性，这表明该模型可以对社交网络上的卖家进行准确分类。