Lili Dong
ABSTRACT
In order to build a reasonable evaluation index system, the selection of indicators is crucial. The selected indicators should preferably comprehensively reflect the performance of the evaluated object. Therefore, this paper proposes a convolutional neural network combing with improved analytic hierarchy process evaluation algorithm (CNN-AHP), and uses this algorithm to systematically evaluate the effect of university industry education integration. Using this algorithm to select relatively independent and representative evaluation indicators, and construct an evaluation indicator system. Then applying the improved Analytic Hierarchy Process (AHP) to calculate the weight of each evaluation indicator; the Convolutional neural network is used to obtain the evaluation score of the integration of production and teaching in higher vocational education, and determine the level of the effect of the integration of production and teaching. Finally, the effectiveness of the evaluation algorithm studied was demonstrated through example testing.
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Index Terms
- A CNN-AHP fusion algorithm for evaluating integration of industry and education
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