Construction of a Dynamic Evaluation Index System for University Majors Based on AI + Big Data Multi-source Distributed Parallel Computing

University major evaluation is a systematic evaluation of the professional construction, teaching quality, and talent training in colleges and universities, aiming to promote construction of professional connotation and improve the quality of talent training. The evaluation is usually organized by government education authorities, third-party institutions, or colleges and universities independently, adopting combination of quantitative and qualitative methods, mainly based on a professional evaluation system that relies on static analysis. The social participation is relatively low. With the comprehensive and in-depth integration of-education integration and science-education integration into the teaching and scientific research work of colleges and universities, the professional construction and development of colleges and universities need more participation from various sectors of. In order to better verify the school-running strength and school-running level of colleges and universities, it is very necessary to construct a professional evaluation system with the participation of subjects, such as government education authorities, colleges and universities, third-party evaluation institutions, and industry enterprises. This kind of multi-party collaborative working mode is a very complex systematic, which not only involves the acquisition of various different information resources and massive data storage and computing, but also needs a complete set of data classification processing and information processing technology. By using "AI + Big Data" technology and adopting a multi-source distributed parallel computing model, it can effectively solve the storage and computing of various information data and greatly avoid the intervention of human in the evaluation process, which is a powerful guarantee for the extensive participation in the evaluation process and the fairness and justice of the evaluation results. 高校专业评估是对高校开设的专业在办学条件、教学质量、人才培养等方面进行的系统性评价，旨在促进专业内涵建设、提升人才培养质量。评估通常由政府教育主管部门、第三方机构或高校自主组织，采用定量与定性相结合的方法，主要是依据基于静态分析的专业评估体系进行，社会参与度较低。随着产教融合、科教融汇全面深入到高校教科研工作中，高校的专业建设与发展更需要社会多方参与。为更好地验证高校的办学实力与办学水平，构建一个由政府教育主管部门、高校、第三方评估机构、行业企业等多主体参与的专业评估体系十分必要。这种多方协同工作模式是一项极为复杂的系统性工程，不仅涉及到各种不同的信息资源获取和海量的数据存储计算，更需要一套完整的数据分类处理与信息加工技术。利用“AI+大数据”技术，采用多源分布式并行计算模式，可有效解决各种信息数据的存储与计算，并能极大地避免人为因素对评估过程的干预，有力保证评估过程的广泛参与和评估结果的公平公正。