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This study puts forward an academic level evaluation model of journal based on a rough-set-equivalent thinking and neural networks and tests the model’s efficiency and practicality by comparing it to the traditional evaluation methods. First of all, the forming of this evaluation model includes the simplification of journal evaluation with theories based on rough-set-equivalent thinking and the abandoning of miscellaneous evaluation indicators. Secondly, the remaining essential evaluation indicators would be used to form plenty of training samples for the neural networks’ building up. Lastly, the neural networks would use the BP algorithm to rank those samples in general and therefore forms the journal academic level evaluation model. In order to testify the effectiveness of this model, other methods of TOPSIS is used to evaluate these journals and gray-relation-based thinking is used to set the essential indicators’ weights, which provide another outcome for comparison. The instance analysis of food journals indicates that the process of building this evaluation model is secured and logical and the model could well fit into the actual food journals academic level evaluation.

Comparative Study of Comprehensive Evaluation Model for Academic Quality of Food Journals