Modeling Analysis of Network Spatial Sensitive Information Detection Driven by Big Data

The dissemination of sensitive information has become a serious social content. In order to effectively improve the detection accuracy of sensitive information in cyberspace, a sensitive information detection model in cyberspace is established under the drive of big data. By using word segmentation and feature clustering, the text features and image features of current spatial data information are extracted, the dimension of the data is reduced, the document classifier is built, and the obtained feature documents are input into the classifier. Using the open source database of support vector machine (SVM) and LIBSVM, the probability ratio of current information belongs to two categories is judged, and the probability ratio of classification is obtained to realize information detection. The experimental data show that, after the detection model is applied, the accuracy of the text-sensitive information detection in the network space is improved by 35%, the accuracy of the image information detection is improved by 29%, and the detection model has the advantages of obvious advantages and strong feasibility.