The CnOpenData AI Positions Online Recruitment Database is an annual recruitment information database centered on artificial intelligence-related positions. This dataset is collected through multi-platform online recruitment channels, using AI position-related keywords as the search criteria for database construction. It employs a specialized AI keyword dictionary (sourced from Yao Jiaquan, Zhang Kunpeng, Guo Lipeng, and Feng Xu, 2024: "How Does Artificial Intelligence Enhance Enterprise Productivity? —Perspectives from Labor Skill Structure Adjustment," Management World, Vol. 2.) to screen AI technology-related job advertisements. The database encompasses key fields such as company name, work location, position title, job description, number of hires, compensation, education requirement(学历要求), work experience(工作经验), and release date(发布日期), providing a robust data foundation for researching AI talent demand structures, position evolution, and technology penetration trends.
Data Uniqueness
- Screening System Based on Authoritative Academic Standards: Leveraging an AI keyword dictionary validated by authoritative journals, it establishes a scientific and reproducible position identification mechanism, ensuring high accuracy in technical relevance and industry representativeness.
- Time-Series Data Covering the Full AI Industry Lifecycle: A ten-year span comprehensively captures key development stages of China's AI industry, from technological exploration to industrial integration, supporting long-cycle studies on technology diffusion, talent structure evolution, and policy evaluation.
- Systematic Integration of Multi-Source Heterogeneous Data: By integrating raw data from three mainstream recruitment platforms, it effectively mitigates sample bias inherent in single-platform data, constructing a more representative sample system in terms of enterprise distribution, regional coverage, and position types.
Data Application Value
- Academic Research: Provides high-quality micro-datasets for studies on technological innovation and labor markets, supporting empirical research on critical economics and management topics such as skill-biased technological change, talent mobility patterns, and occupational structure transformation. Based on job descriptions and skill requirements, AI technology diffusion indices and skill complexity metrics can be constructed to advance quantitative research in digital economics.
- Business Decision-Making: Enables enterprises to precisely grasp AI talent market dynamics. By analyzing salary levels, benefit composition(福利构成), and recruitment scale, companies can optimize talent attraction and retention strategies. Supports investment institutions in identifying frontier technology firms and assessing innovation potential and industry positioning through monitoring corporate AI talent pools and technical directions.
- Policy Evaluation: Offers critical evidence for evaluating regional digital economy development levels. By analyzing the spatial distribution and mobility patterns of AI talent, it assists local governments in formulating targeted talent recruitment and industrial support policies. Helps education authorities optimize higher education resource allocation by aligning industry skill demands with academic program designs, enhancing the adaptability of talent cultivation systems to labor market needs.
With its authoritative methodology, comprehensive temporal coverage, and rich dimensional information, this database has become vital infrastructure for studying AI industry development and talent market evolution. Its systematic architecture meets both the rigor of academic research and the practical needs of policy-making and business decisions, offering irreplaceable value in digital economy research.
The dictionary content is detailed below:
Time Period
May 2014 - 2024
Data Scale
Field Display
Sample Data
AI岗位线上招聘数据-B来源
AI岗位线上招聘数据-C来源
AI岗位线上招聘数据-E来源
参考文献
- 孙鲲鹏、罗婷、肖星，2021：《人才政策、研发人员招聘与企业创新》《经济研究》第8期。
数据更新频率
年度更新