More and more image materials are used in various industries these days. Therefore, how to collect useful images from a large set has become an urgent priority. Convolutional neural networks (CNN) have achieved good results in certain image classification tasks, but there are still problems such as poor classification ability, low accuracy, and slow convergence speed. This article mainly introduces the image classification algorithm (ICA) research based on the multilabel learning of the improved convolutional neural network and some improvement ideas for the research of the ICA based on the multilabel learning of the convolutional neural network. This paper proposes an ICA research method based on multilabel learning of improved convolutional neural networks, including the image classification process, convolutional network algorithm, and multilabel learning algorithm. The conclusions show that the average maximum classification accuracy of the improved CNN in this paper is 90.63%, and the performance is better, which is beneficial to improving the efficiency of image classification. The improved CNN network structure has reached the highest accuracy rate of 91.47% on the CIFAR-10 data set, which is much higher than the traditional CNN algorithm.

中文翻译：

---

基于大数据和改进卷积神经网络多标签学习的图像分类算法

如今，越来越多的图像材料用于各个行业。因此，如何从大集合中收集有用的图像成为当务之急。卷积神经网络（CNN）在某些图像分类任务中取得了不错的效果，但仍存在分类能力差、准确率低、收敛速度慢等问题。本文主要介绍了基于改进卷积神经网络多标签学习的图像分类算法（ICA）研究以及基于卷积神经网络多标签学习的ICA研究的一些改进思路。本文提出了一种基于改进卷积神经网络多标签学习的ICA研究方法，包括图像分类过程、卷积网络算法、和多标签学习算法。结论表明，本文改进后的CNN平均最大分类准确率为90.63%，性能更好，有利于提高图像分类效率。改进后的CNN网络结构在CIFAR-10数据集上达到了91.47%的最高准确率，远高于传统的CNN算法。