Innovative Application of Medical Image Classification Technology Driven by Hybrid Attention Network in E-Commerce Medical Scenarios

在电子商务与医疗健康产业深度融合的时代背景下，医学图像的自动化精准分类成为提升在线医疗服务效率的核心技术瓶颈。本文提出一种融合卷积神经网络(CNN)局部特征提取能力与Transformer全局建模优势的混合注意力网络(Medical Hybrid Attention Net, MHAN)，通过构建“多尺度特征金字塔–双注意力增强–跨层级特征交互”的三级技术架构，实现对皮肤病变、消化道息肉复杂医学图像的高效语义解析。在两大公开数据集上的实验表明，该模型准确率达75.80%，较传统卷积网络与纯Transformer模型提升显著。结合电商平台业务逻辑，深入探讨其在在线诊断服务集成、健康产品智能推荐系统、医疗商品质量闭环管理中的创新应用模式，为“互联网 + 医疗健康”的智能化升级提供了完整的技术解决方案与商业落地路径。In the era of deep integration of e-commerce and medical health industry, the automatic and accurate classification of medical images has become the core technical bottleneck for improving the efficiency of online medical services. This paper proposes a hybrid attention network (Medical Hybrid Attention Net, MHAN) that integrates the local feature extraction capability of convolutional neural network (CNN) and the global modeling advantage of Transformer. By constructing a three-level technical architecture of “multi-scale feature pyramid-dual attention enhancement-cross-level feature interaction”, it realizes efficient semantic parsing of complex medical images such as skin lesions, gastrointestinal polyps. Experiments on two public datasets show that the accuracy of the model reaches 75.80%, which is significantly improved compared with traditional convolutional networks and pure Transformer models. Combined with the business logic of e-commerce platforms, this paper deeply explores its innovative application models in online diagnostic service integration, intelligent recommendation system for health products, and closed-loop management of medical product quality, providing a complete technical solution and commercial landing path for the intelligent upgrade of “Internet + Medical Health”.