多尺度特征融合CNN的高效电商商品分类研究
Research on Efficient E-Commerce Commodity Classification Using Multi-Scale Feature Fusion CNN
摘要: 针对电商图像商品尺度跨度大、背景干扰多及分类实时性要求高等挑战，本文提出一种多尺度特征融合的高效卷积神经网络模型MSFF-EComNet。首先，设计多尺度并行卷积架构(MSFF)，利用多分支感受野同步捕获商品的局部细节与全局语义特征；其次，引入改进的注意力协同机制(IASM)，通过空间与通道维度的联合增强，引导模型精准聚焦商品主体并有效抑制背景噪声。实验结果表明，在DeepFashion数据集上，该模型的精确率达到89.56%。相较于经典ResNet-50，本文模型准确率提升4.44%，参数量压缩54%。该研究为大规模电商场景下的图像识别提供了一种兼顾精度与效率的平衡方案。
Abstract: To address the challenges of large-scale variations in e-commerce product images, numerous background interferences, and high real-time classification requirements, this paper proposes an efficient convolutional neural network model, MSFF-EComNet, featuring multi-scale feature fusion. First, a multi-scale parallel convolution architecture (MSFF) is designed to simultaneously capture local details and global semantic features through multi-branch receptive fields. Second, an improved attention synergy mechanism (IASM) is introduced to enhance spatial and channel dimensions, guiding the model to precisely focus on the main object while effectively suppressing background noise. Experimental results demonstrate that the model achieves an accuracy of 89.56% on the DeepFashion dataset. Compared to the classic ResNet-50, our model achieves a 4.44% accuracy improvement while reducing parameters by 54%. This research provides a balanced solution that prioritizes both accuracy and efficiency for image recognition in large-scale e-commerce scenarios.
文章引用：张泰恒. 多尺度特征融合CNN的高效电商商品分类研究[J]. 电子商务评论, 2026, 15(5): 84-91. https://doi.org/10.12677/ecl.2026.155491

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