Abstract: With the advent of the Internet economy era,the efficient management of e-commerce platforms has garnered widespread attention from both academia and industry.Among various factors,the accuracy and automation level of product classification directly impact users' experience and the optimization of operational efficiency.In light of this,this study delves into the latent space representation of product information,proposing a blended learning analysis paradigm for product latent space representation(BEML).This framework integrates advanced bidirectional encoder representations from transformers(BERT) techno-logy with traditional machine learning methods,aiming to significantly enhance the efficiency and accuracy of automated product classification through detailed analysis of the latent space of product information.By conducting comparative analysis with current mainstream deep learning and machine learning algorithms,this study validates the exceptional performance of the BEML framework in product classification tasks.Experimental results demonstrate that the BEML framework achieves a macro F1 score of 85.79% and a micro F1 score of 84.73%.Both exceed the current best F1 score of 83.3%,reaching a state of the art.Moreover,this framework not only represents a theoretical innovation but also holds significant practical application value in the realm of information management and automation processing within the e-commerce sector,providing an efficient and reliable blended lear-ning analysis paradigm for the field of technology and business.

Key words: Latent space representation, Pre-trained BERT model, Automated commodity classification, Intelligent commodity classification, Sci-tech driven business