Occupational classification refers to the process of categorizing social professions with similar general or essential characteristics into specific classification systems according to defined rules and standards. With societal development and progress, occupations evolve rapidly. Beyond the obsolescence of obsolete roles and the emergence of new positions, the activities and methodologies within the same occupation undergo continuous transformation. Consequently, occupational classification possesses distinct epoch-specific characteristics. Broadly speaking, when occupations were fewer, "occupation" and "industry" were synonymous terms. Today, occupational classification distinguishes between occupation and industry as interrelated yet distinct concepts. In occupational classification, industries often function as occupational categories. Spatially, occupational distribution exhibits variations across regions, urban-rural divides, industries, and national boundaries.

  Applications of occupational information for enterprises and human resources departments:

• Rapidly and effortlessly develop effective job descriptions.
• Expand the talent pool of qualified candidates for open positions.
• Define critical success factors for employee roles.
• Align organizational development with workplace demands.
• Refine recruitment and training objectives.
• Design competitive compensation and promotion systems.

  Applications of occupational information for job seekers:

• Match jobs to their interests, skills, and experience.
• Explore emerging occupations using current labor market data.
• Research strategies to secure ideal employment.
• Maximize earning potential and job satisfaction.
• Understand key factors for success within their professional field.

  CnOpendata introduces O*NET Occupational Characteristics Data for the United States, covering over 1,000 occupations. Based on the 2018 International Standard Classification of Occupations (ISCO), this dataset encompasses occupation-related skills, abilities, knowledge, work activities, and interest profiles. Such information facilitates occupational exploration, career counseling, and diverse human resources research.

Database Application Guide

CnOpenData O*NET U.S. Occupational Characteristics Data Application Framework:
https://mp.weixin.qq.com/s/lSoh5qUZ4kwUYeadY6wQgA

Temporal Coverage

Cross-sectional data: September 2025 (can be updated upon request)

Field Specifications

Sample Data

Given the complex structure of this dataset with numerous sub-tables, this overview provides only partial samples. Detailed field indicators and sample data can be accessed by visiting the respective module subpages in the right-hand tab.

职业基本信息表

Relevant Literature

• Lena Hensvik, Thomas Le Barbanchon, Roland Rathelot, 2021, “Job search during the COVID-19 crisis”, Journal of Public Economics, Volume 194.
• Chen Lin, Gao Yuepeng, Yu Linhui, 2024, “How Does Artificial Intelligence Transform Corporate Labor Demand? Analysis Based on Big Data from Recruitment Platforms”, Management World, No. 6.

Data Update Frequency

Annual updates