This article aims to improve the accuracy of real-time image recognition in the context of the Internet of Things (IoT), reduce the core network pressure of the IoT and the proportion of IoT broadband, and meet people’s demand for internet image transmission. An intelligent image fusion system based on mobile edge computing (MEC) and deep learning is proposed, which can extract the features of images and optimize the sum of intra-class distance and inter-class distance relying on the hierarchical mode of deep learning, and realize distributed computing with the edge server and base station. Through comparison with other algorithms and strategies on the text and character data sets, the effectiveness of the constructed system is verified in the performance of the algorithm and the IoT. The results reveal that the application of the unsupervised learning hierarchical discriminant analysis (HDA) has better accuracy and recall in various databases compared with conventional image recognition algorithms. When the sum intra-class and inter-class distance K is 2, the accuracy of the algorithm can be as high as 98%. The combination of MEC and layered algorithms effectively reduces the pressures of core network and shortens the response time, greatly reduces the broadband occupancy ratio. The performance of IoT is increased by 37.03% compared with the general extraction and common cloud computing. Image recognition based on the MEC architecture can reduce the amount of network transmission and reduce the response time under the premise of ensuring the recognition rate, which can provide a theoretical basis for the research and application of user image recognition under the IoT.

本文旨在提高物联网（IoT）上下文中实时图像识别的准确性，减轻IoT的核心网络压力和IoT宽带的比例，并满足人们对Internet图像传输的需求。提出了一种基于移动边缘计算（MEC）和深度学习的智能图像融合系统，该系统可以根据深度学习的层次化模式提取图像特征，优化类内距离和类间距离之和，与边缘服务器和基站一起实现分布式计算。通过与文本和字符数据集上的其他算法和策略进行比较，在算法和物联网的性能上验证了构建系统的有效性。结果表明，与传统的图像识别算法相比，无监督学习分层判别分析（HDA）的应用在各种数据库中具有更好的准确性和召回率。当类内和类间距离之和K为2，该算法的精度可以高达98％。MEC和分层算法的结合有效地降低了核心网的压力，缩短了响应时间，大大降低了宽带占用率。与通用提取和通用云计算相比，IoT的性能提高了37.03％。基于MEC架构的图像识别可以在保证识别率的前提下，减少网络传输量，缩短响应时间，为物联网下用户图像识别的研究和应用提供理论依据。