Automatic classification method of e-commerce commodity raw materials through the introduction of self-supervised concepts and the construction of domain ontology

The e-commerce platform's function-oriented classification basis will cause items with the same (different) raw materials to be incorrectly classified into different (same) functional categories, posing a challenge to marketing staff who create item sales statistics based on raw materials. Furthermore, it is challenging to promote the present item classification method in engineering applications since it necessitates a high number of manual markings to add labels. As a result, this paper created an item conceptual model to specify the categories and attributes of item raw materials, allowing it to screen item specification samples and automatically add category labels, generate domain-specific lexicon to extract item raw material features, and finally use a machine learning classifier to complete the classification. This research presents a verification of the suggested classification model using flour data from the Chinese e-commerce platform. The experimental results show that the self-supervised learning-based classification method proposed in this article for classifying raw materials of e-commerce items can achieve an accuracy of 91%.