Abstract:

Data annotation, as a key link in the foundational layer of the artificial intelligence industry, directly affects the performance of artificial intelligence (AI) algorithm models and the implementation of application scenarios, and is the core productive force of high-quality AI datasets. This paper systematically reviews the connotation definition, industrial chain structure, development model and policy environment of the data annotation industry, presents a detailed summary of the application practices regarding how the data annotation industry empowers the “AI+” initiative across key sectors, deeply analyzes the industrial transformation brought about by the technological innovation of large models such as DeepSeek, and summarizes the core problems existing at present, such as the lack of top-level design, talent bottlenecks, and insufficient technological collaboration. This study proposes that the high-quality development of the data annotation industry should be promoted through paths such as strengthening the demonstration effect of national-level annotation bases, improving the technical level of data annotation, continuously advancing the application of “AI+” in key industries, constructing a collaborative innovation ecosystem, improving the standard system, and deepening international cooperation.

Key words: AI+, data annotation, high-quality dataset, industry application