The high anonymity of Darknet makes it attractive to users who want to avoid Internet censorship and surveillance. As a result, in recent years, Darknet is abused for various illegal purposes. Undoubtedly, measurement and analysis towards the attributes of people in the Darknet can obtain a comprehensive characterization of dangerous users and help trace malicious users, reducing cybercrimes. However, it is still challenging to extract person attributes in Darknet scenario due to its anonymity and content sparsity. Therefore, in this paper, we propose a new person attribute extraction method consisting of three steps: block filtration, attribute candidate generation and attribute candidate verification. Experiments show that our extraction method performs better than traditional extraction methods. Using the extracted information as input, we measure and analyze the number of attributes, Top-K name entities, email domain name, etc. of people in Darknet, revealing the characteristics of the person attributes in the dark web pages.