Artificial intelligence (AI) is an interdisciplinary subject based on computer science and has a history of more than 70 years. With the development of social industries, various service needs are developing rapidly, and AI is also playing a huge role in the application field of big data. This paper focuses on the analysis and application of AI in the subject classification system for high school students. The subject classification system adopts the characterization of subjects that a country have; and the amount of information data is huge. Different countries set up different subject systems according to the needs of social structure. In 2013, Qingdao Warwick International Education College (Warwick College) and Korean wisemento Co., Ltd. (Wisemento company) spent 4 years jointly researching and completing an analysis and assessment system suitable for Chinese subject classification system. Follow the subject and professional scoring method, the assessment system scientifically calculates trend scores and interacts with potential and career models to find the subject development direction, professional trend, future potential development direction, subject and career direction that are suitable for students. It provides trend data suitable for students’ subject development, provides scientific basis for students’ course selection, helps students understand themselves, and provides students with targeted career education development directions for subjects and careers. The education resources can be utilized effectively; student individual data also provides a basis for the management of education and teaching.

人工智能(简称AI)是以计算机科学为基础的交叉学科，发展至今已有70多年的历史。 随着社会行业的发展，人们的服务需求快速发展，AI在大数据的应用领域也发挥着巨大作 用。本文主要阐述AI在学科分类系统中，对高中生阶段学科数据的分析与应用。学科体系 是采取一个国家应有的学科定性，信息数据量庞大，不同国家根据社会结构的需求设置不 同的学科体系。2013年青岛华威国际教育学院（简称华威学院）与韩国Wisemento公司 （简称Wisemento）历时4年时间，共同研究完成了适合于中国学科体系测评数据的分析与 应用系统。遵循学科及专业类评分法、科学的计算趋向性分值、并与潜能及职业模型交互 的一致性，寻找出适合学生的学科发展方向、专业类趋向、未来潜能发展方向、学科与职 业方向。提供适合学生学科发展的趋向性数据，这对学生课程选择提供了科学依据，帮助 学生认知自我，为学生提供了针对性的学科与职业的生涯教育发展方向。教育资源的有效 利用提供了保障，学生群体与个体性数据为教育教学的管理提供了依据。