Cultivating graduate students' innovative capabilities is an essential goal of graduate education. The evaluation of innovative capabilities helps identify students' unique abilities and potential, promotes the adjustment of educational methods, guides universities to optimize curriculum settings and training strategies, thereby enhancing graduate students' practical application capabilities and research levels. This paper establishes an evaluation model for the cultivation of academic graduate students' innovative capabilities based on the Self-Organizing Feature Map neural network. Relevant data is collected through survey questionnaires, and the Self-Organizing Map neural network model is implemented using MATLAB, with appropriate input data and features selected, and the structure and parameters of the Self-Organizing Network are determined. Finally, the Self-Organizing Network model is used to classify and score academic graduate students at a university in Sichuan. 

Bin Pan, Hongxia Guo and Zetong Tang. Evaluation of Graduate Student Innovation Capability Cultivation Based on Self-Organizing Networks. International Journal of Educational Innovation and Science (2024), Vol. 5, Issue 1: 42-52. https://doi.org/10.38007/IJEIS.2024.050105.

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