Affiliations: Lishui Vocational & Technical College, Lishui, China
Correspondence:
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Corresponding author. Jun Ye, Lishui Vocational & Technical College, Lishui, 323000, China.
E-mail: [email protected].
Abstract: The traditional teaching quality evaluation model can only evaluate the data of the corresponding period, does not have the deep learning function, and cannot realize the prediction based on the existing data. In order to improve the operation effect of the teaching evaluation model, this study built a teaching evaluation model by combining data mining with deep learning and constructed a data processing module based on cloud computing. Moreover, this study used the improved incomplete multi-classification algorithm to construct the multi-divider and applied the constructed classifier to the teaching evaluation system to automatically classify the teachers. In addition, after learning the relevant theoretical knowledge of the university evaluation system, this paper also designed a prototype of the teaching quality evaluation framework based on the improved algorithm. Through the analysis of the model effect, it can be seen that the results of this study have certain evaluation effects, which can be applied to practice, and can provide theoretical reference for subsequent related research.
Keywords: Deep learning, cloud computing, big data, teaching quality, machine
