<![CDATA[The rapid growth of e-commerce platforms has led to the massive accumulation of product thumbnail images, making manual visual analysis inefficient and conventional sequential processing methods insufficient to handle such data volumes in a timely manner. Given the crucial role of thumbnails in influencing consumer purchasing decisions, computational strategies are required to accelerate the analysis process without compromising classification accuracy. This study applies a parallel computing approach combined with deep learning to improve the efficiency of visual trend analysis using two primary datasets: 2,608 images for model training and validation, and 40,254 images for large-scale inference. The proposed framework integrates parallel image preprocessing on multi-core CPUs, the development of a Convolutional Neural Network based on MobileNetV2 using a transfer learning approach, and batch-based parallel inference on GPUs. The developed model demonstrates stable and convergent performance, achieving a training accuracy of 0.85 and a validation accuracy of 0.83. Efficiency testing during the preprocessing stage shows that the parallel approach is more effective under large data workloads, providing a speed improvement of up to 1.58×. During the inference stage, predictions for 500 images can be completed in 1.84 seconds compared to 41.76 seconds using the sequential method, resulting in a significant computational speedup of 22.8×. Big data analysis reveals a polarization of visual strategies, where technology product categories are dominated by infographic-style thumbnails, fashion categories rely heavily on human model representations, and household product categories emphasize clean product visuals supported by promotional elements. This study concludes that the application of parallel computing significantly enhances the efficiency and scalability of visual big data analysis in e-commerce and supports more operational and strategic mapping of visual trends.]]>
