University Innovation Capability Evaluation System Based on Big Data Technology

Along with the fast developing of Large Data and AI, the assessment of innovative capability of university students is becoming an important trend in the reformation of higher education. An innovative assessment system for university students' innovative competence was developed by using large data analysis technique, and proposed a scientific and accurate innovation ability evaluation method by combining ant colony optimization clustering algorithm and fuzzy evaluation model. By constructing a multidimensional evaluation index system, the system comprehensively evaluates the innovation ability of college students from multiple perspectives such as innovative thinking, team collaboration, and problem-solving ability. In terms of data collection, the system adopts multi-source heterogeneous data fusion based on big data, and uses ant colony optimization clustering algorithm for data mining to ensure accuracy and efficiency in the evaluation process. The fuzzy evaluation model improves the reliability of the evaluation results by dynamically adjusting weights and handling uncertainty. The experimental results show that the evaluation system proposed by the author is superior to traditional analytic hierarchy process and mean clustering method in terms of evaluation accuracy and efficiency, and has high practical value and promotion prospects. The system not only supplies the science foundation to cultivate and manage innovative ability, but also provides some new thinking and practice for the exploration of GDW in educational domain.