Abstract
Scientific research ability (SRA) is now the main technical index used to assess comprehensive strength of a university. At the same time, it will have an impact on college and university rankings as well as enrollment. However, at the moment, college and university evaluation standards for research capacity are not unified, and there are various evaluation criteria, making it impossible to measure with unified standards. The main reason is that the evaluation factors are not consistent. Based on the characteristics of Guangxi Normal University of Science and Technology (GNUST), this paper develops the evaluation system of GNUST and introduced an improved neural network to assess the SRA of the faculty and the staff of GNUST. The simulation results show that the 15 technical indicators used in this paper are effective.
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