An Image Classification and Retrieval Algorithm for Product Display in E-Commerce Transactions

E-commerce has unmatched advantages over conventional ways of consumption, but it also restricts the amount of contact that customers have with the things they are purchasing. The first step in the transaction product information flow is an effective transaction product display image, which is crucial for product sales. The influence of the emotions present in the current transaction product display image on the buyer's purchase intention has not been established, and as the number of commodity classes rises, there is also a lack of scientific guidance regarding the buyer's efficient retrieval of transaction products. Therefore, this work investigates image classification and retrieval methods for product presentation in e-commerce transactions. A robust network architecture was created for the e-commerce transaction product display and image classification. The image polarity emotion feature extraction backbone module, the polarity emotion intensity perception module, and the emotion feature fusion classification module are the three specific components of the model. The network training approach is an innovation to address the issue that the polarity emotion intensity cannot be effectively conveyed in the e-commerce transaction product display image. The research improves the design of the similarity retrieval algorithm for ecommerce transaction product display images, which increased the retrieval effectiveness for buyers. The correctness of the classification model and retrieval approach was confirmed by the experimental results.