With the advent of the era of network information, the amount of data in network information is getting larger and larger, and the classification of data becomes particularly important. Current semi-supervised multi-map classification methods cannot quickly and accurately perform automatic classification and calculation of information. Therefore, this paper proposes an MR-MGSSL algorithm and applies it to the classification of semi-supervised multi-graph. By determining the basic idea and calculation framework of MR-MGSSL algorithm, the mining of optimal feature subsets in multi-graphs and the multi-graph vectorization performance time are taken as examples, and the proposed algorithm is compared with other semi-supervised multi-graph classification methods. The performance evaluation results show that compared with other classification calculation methods, MR-MGSSL algorithm has the advantages of low sensitivity to feature subgraph and short vectorization time. The method is used to extract and detect clouds in remote sensing images (GF-1 and GF-2).

随着网络信息时代的到来，网络信息中的数据量越来越大，数据的分类变得尤为重要。当前的半监督多地图分类方法无法快速，准确地执行信息的自动分类和计算。因此，本文提出了一种MR-MGSSL算法，并将其应用于半监督多图的分类。通过确定MR-MGSSL算法的基本思想和计算框架，以多图最优特征子集的挖掘和多图矢量化的执行时间为例，将该算法与其他半监督多图算法进行比较。图分类方法。性能评估结果表明，与其他分类计算方法相比，MR-MGSSL算法对特征子图的敏感性低，向量化时间短。该方法用于提取和检测遥感图像（GF-1和GF-2）中的云。