Deepen the application of artificial intelligence in government services, urban governance, rural revitalization and other fields, prepare public data labeling catalogs, and promote public data labeling and development and utilization in an orderly manner in accordance with laws and regulations.

Support public data to empower the development of the real economy, and explore public data labeling needs in key areas such as modern agriculture, intelligent manufacturing, and information services.

Support the application of public data fusion across departments, regions and levels, and encourage government departments and enterprises to collaborate on the annotation and training of data required for government big models.

Promote the inclusion of data labeling services into the scope of government procurement

Strengthen data labeling in key industries such as transportation, medical care, finance, science, manufacturing, and agriculture, build high-quality industry data sets, and support the application empowerment of artificial intelligence in the industry field

Focusing on medical and health, human resources, digital trade, autonomous driving, low-altitude economy and other scenarios, business innovation drives data to mark demand

Strengthen the key technology research and application in the fields of cross-domain cross-modal semantic alignment, 4D annotation, large model annotation and other data annotation

Support the research and development of intelligent tools such as multi-modal annotation, labeling review, quality evaluation, and expert annotation based on thinking chains.

Support the construction of an innovative data labeling platform integrating data, models, tools and scenarios

Supports the research and development of key equipment in the field of integrated software and hardware, independent and controllable data labeling

Establish a framework for data labeling standards and formulate national standards such as data labeling technology, quality, and capabilities

Cultivate and build key laboratories, technology innovation centers in the field of data labeling

Support data labeling enterprises to jointly establish innovation platforms and collaborative innovation bases for industry-education integration, etc.

Encourage relevant enterprises and scientific research institutions to participate in the construction of open source communities and promote the joint construction and sharing of innovative resources.

Cultivate a group of leading data labeling enterprises, encourage them to become bigger and stronger through resource integration, mergers and acquisitions, and promote the scale, standardization and intensive development of data labeling enterprises

Cultivate a group of data-labeled gazelle enterprises and unicorn enterprises that are deeply involved in the industry

Promote accurate connection between data labeling SMEs and third-party institutions such as human resources, financial services, and compliance consultation to help enterprises develop rapidly

Support data labeling leading enterprises and third-party institutions to build open source data labeling platforms to help small and medium-sized enterprises develop

Cultivate a group of third-party institutions for service data labeling of human resources, supply and demand connection, international cooperation, legal audit, etc., and improve the data labeling industry ecology

Carry out innovation forums, collection of scenario cases, academic exchanges and other activities in the field of data annotation, and select excellent cases of data annotation

Encourage the holding of data labeling innovation competitions and other events, strengthen the promotion of data labeling innovation achievements and scenario expansion

Support enterprises and institutions to take the lead in formulating international standards for data labeling

Relying on my country's digital infrastructure advantages, domestic enterprises are encouraged to undertake international business of data labeling

Encourage places with conditions to increase support for the data labeling industry

All regions and departments can coordinate the procurement costs of data products and labeling services based on actual conditions.

Make full use of data coupons, algorithm coupons and computing power coupons issued by various places to reduce the cost of data labeling enterprises

Encourage all types of industrial funds and professional investment institutions to increase investment in data labeling, and guide social capital to participate in an orderly manner.

Build a public service platform for data labeling, strengthen dynamic monitoring of labeling industries, and improve capabilities such as industrial resource aggregation, supply and demand docking, and risk management

In accordance with the requirements of standardized access and interconnection, we will create a national "one network" for public services for data labeling

Made (revise) the national occupational standards for artificial intelligence training and data labeling

Deepen the integration of industry, academia and research, encourage industry alliances, universities, research institutes and enterprises to establish long-term cooperation mechanisms, and increase cooperation in data labeling practice projects, continuing education and public training base construction

Relying on industry organizations, colleges, social training evaluation organizations, etc., we will carry out data marking related professional skills level certification

Support the connection and mutual recognition of professional qualifications and professional skill levels in the field of data labeling to smooth the talent development channel

Support the construction of data labeling talent database at a level and strengthen industrial talent support

Establish and improve relevant standards for data labeling safety risk identification, monitoring and early warning, emergency response, etc., and implement the safety responsibilities of relevant entities in the entire process of data labeling

Reasonably protect the relevant rights and interests formed by data labeling enterprises in the process of data flow

Strengthen the construction of data labeling privacy protection, artificial intelligence alignment, and security assessment capabilities