Design and Implementation of University Students Scientific Research Ability Evaluation System Based on Neural Network

Abstract

At present, many colleges attach more and more importance to the cultivation of scientific research ability of college students. However, there is no unified standard to effectively measure the cultivation effect. The current performance inference method cannot comprehensively evaluate the scientific research ability of college students. Few scholars have directly analyzed the college students’ scientific research ability from the internal and external influencing factors. Therefore, this paper tries to design and implement a neural network analysis system for scientific research ability evaluation of college students. After surveying the status quo of scientific research ability evaluation of college students in northern China’s Hebei Province, a hierarchical evaluation index system was constructed for college students’ scientific research ability, referring to the existing evaluation index systems, and the weights were designed for the evaluation indices. Next, the backpropagation (BP) neural network was optimized by chaotic sine-cosine grasshopper optimization algorithm (CSCGOA) and used to establish a neural network analysis system for scientific research ability evaluation of college students. The proposed system was proved effective through experiments. The relevant results effectively enhance the scientific level and accuracy of the evaluation of college students’ scientific research ability, improve the cultivation of college students, and provide a scientific basis for colleges to understand the scientific research ability of their students.