## 1 Introduction  
Amidst the rapid expansion of the Internet, concerns regarding network security have become increasingly prominent. Notably, the proliferation of illicit websites, including those involved in gambling, pornography, and fraudulent activities, has witnessed a persistent surge, thereby imposing a grave peril to individuals' financial assets and information security. According to \"Facts and Figures\" released by ITU [1], as of December 2022, about 52.3% of users have been harassed by illegal websites. Illegal websites not only inflict financial losses upon their victims but also compromise personal privacy and sensitive information, with potential repercussions on national security as well. The regulation of illegal websites is confronted with numerous obstacles and challenges. The prompt and accurate discovery and mitigation of such illicit platforms from a technical standpoint have emerged as pressing issues demanding immediate solutions. Hence, this research paper endeavors to explore an extensive and expedited approach for the detection of illegal websites. Additionally, it aims to examine the utilization of technical means to enhance the efficacy and precision of governing such illicit platforms, thereby ensuring the wholesome development of the Internet ecosystem. Typically, existing research on combating illegal websites primarily concentrates on identifying platforms associated with gambling and pornography. These studies often rely on inputting specific website information such as text or images into detection models, which then render judgments on the legality of the website. This approach is crucial for detecting illicit websites. However, when dealing with large-scale and constantly evolving illicit websites, this method struggles to adapt to the disruptive changes posed by these websites and to effectively control the entire illicit ecosystem. On the other hand, utilizing clustering methods allows for identification from a global sample perspective, enabling adaptive classification detection based on the similarities between samples. To achieve rapid monitoring and comprehensive control of the entire illegal landscape, innovative governance solutions need to be adopted. Cyberspace surveying and mapping entails the detection of diverse assets within the cyberspace, acquiring various attributes associated with these assets, and subsequently conducting fusion processing and correlation analysis on the collected network asset data to construct a comprehensive map of cyberspace resources. The cyberspace surveying and mapping engine continuously scans the network assets across the entire network, capturing and retaining information pertaining to all network websites. This valuable resource provides crucial data support for the governance of illegal websites. Therefore, in this research paper, we utilize the website asset data from the network surveying and mapping platform to perform image-text comparative learning (Section 3.1), generating representations of website screenshots and the corresponding text. These representations serve a dual purpose: they facilitate subsequent website clustering for the discovery of illegal websites and expand the search capabilities of the network surveying and mapping platform. Additionally, we employ the semantic features obtained through image-text comparative learning to conduct cluster analysis and systematically identify illegal websites. Through the analysis of 3.7 million website asset data entries within the network asset mapping platform, we successfully identified 397,275 instances of illegal websites. In summary, our study offers the following contributions:  
* We present a novel approach that combines the extensive data support provided by the network mapping engine. We modify the text model and employ transfer learning techniques to adapt it to the Chinese language environment, building upon the principles of the CLIP model. Additionally, we leverage unsupervised comparative learning to extract semantic features from website assets.