A Study on Multimodal Product Understanding and Assembly Guidance Optimization in E-Commerce for Complex Consumer Goods

This research deals with the common problems of "not knowing the product before buying and assembling after buying" in the context of online sales of complex consumer products. A multimodal product understanding and assembly guidance optimization approach is proposed to address the information gap between product understanding, feature recognition and assembly execution. The research focuses on consumer products, such as bicycles, which are characterized by multiple components, significant parameters, and reliance on user self-assembly. This scenario is in close agreement with the applicant's actual experience in designing product display images, functional annotations, and installation instructions.Methodologically, the research uses CLIP for the semantic alignment of product images and text, BLIP-2 for the completion of text description and step consistency verification, and for the extraction of key structure visual characteristics. Additionally, a Transformer model encodes product parameter descriptions, component hierarchies, and assembly sequence information.On the basis of this framework, we construct a model of product understanding and a prediction model of assembly task. The User Behavior Analysis includes funnel analysis and Cognitive Load Stratification Assessment, and A/B Test is used to compare the performance of a conventional product page with an optimized solution at the time of purchasing decision-making and assembly execution. The experimental data include 52 product models, 6480 product page interaction records, 1,940 assembly task data points, and 14 months after sales service records.The results show that the optimized solution can increase the precision of product feature comprehension by 33.8%, the average time of assembly task is reduced by 26.9%, and the number of post-sales queries is reduced by 35.7%, and the relevant return request is reduced by 18.4%. Further results show that the problem of semantic mismatch between text and image has been reduced by over 40% with CLIP and BLIP-2. Research shows that the display system for complex consumer products should not be restricted to static visual presentation, but should serve as a digital information infrastructure that integrates product recognition, task orientation and after-sales support. This study is of practical importance to increase the transparency of electronic commerce for complex products, to reduce the risk of false tracking and misassembly, and to optimize the consumer experience.