CnOpenData AI Job Recruitment Data is an annual recruitment information database centered on artificial intelligence-related positions. This dataset is collected through multi-platform online recruitment channels, using AI-related keywords as the core criteria for database construction. It employs a specialized AI keyword dictionary (derived from Yao Jiaquan, Zhang Kunpeng, Guo Lipeng, Feng Xu, 2024: How Does Artificial Intelligence Enhance Enterprise Productivity? ——Perspective from Labor Skill Structure Adjustment, Management World, No. 2.) to filter AI technology-related job advertisements. The database covers key fields including company name, work location, position title, job description, number of recruits, compensation, education requirements, work experience, and release date, providing a robust data foundation for studying AI talent demand structure, position evolution, and technology penetration trends.

Data Uniqueness

• Screening System Based on Authoritative Academic Standards: Leverages an AI keyword dictionary validated by authoritative journals to establish 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: Captures key developmental stages of China's AI industry from technological exploration to industry integration over a ten-year span, supporting long-cycle technology diffusion studies, talent structure transition analysis, and policy evaluation research.
• Systematic Integration of Multi-Source Heterogeneous Data: By integrating raw data from three mainstream recruitment channels, effectively mitigates sample bias from single platforms, 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 technological innovation and labor market research, supporting empirical studies on skill-biased technological progress, talent mobility patterns, and occupational structure transformation—critical topics in economics and management. Based on job descriptions and skill requirements, enables the construction of AI technology diffusion indices and skill complexity metrics, advancing quantitative research in digital economics.
• Business Decision-Making: Helps enterprises accurately grasp AI talent market dynamics. By analyzing salary levels, benefit compositions, and recruitment scales, optimizes talent attraction and retention strategies. Supports investment institutions in identifying frontier technology firms by monitoring AI talent reserves and technical directions to assess innovation potential and industry positioning.
• Policy Evaluation: Provides key evidence for evaluating regional digital economy development levels. By analyzing AI talent distribution and mobility patterns, supports local governments in formulating precise talent introduction and industry support policies. Assists education authorities in optimizing higher education resource allocation by comparing industry skill demands with university curricula to enhance alignment between talent cultivation systems and 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 labor market evolution. Its systematic architecture meets both the rigor of academic research and the practical needs of policymaking and business decisions, offering irreplaceable value in digital economy research.

Dictionary contents are detailed in the table below:

Time Period

2014.5–2024

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