Study on the Environmental Innovation Capability Evaluation Model of Manufacturing Enterprises Based on Entropy Weight TOPSIS-BP Neural Network and Empirical Research

Abstract

In order to objectively and accurately evaluate the environmental innovation capability of manufacturing enterprises, this study establishes an evaluation indicator system of environmental innovation ability of manufacturing enterprises. We propose the evaluation model of environmental innovation capability of manufacturing enterprises based on the integrated learning algorithm which is entropy weight TOPSIS and BP neural network. First, the entropy weight method is employed to calculate the weighted index and comprehensive evaluation of environmental innovation capability of manufacturing enterprises by TOPSIS method. Then, the evaluation value is used as a priori sample for the training and testing of BP neural network. The environmental innovation ability of the manufacturing enterprises is analyzed and evaluated in a more comprehensive way. Furthermore, an empirical evaluation of the sixty enterprises in Heilongjiang Province is taken as the example to illustrate the feasibility of this method. And the environmental innovation capability of enterprises is comparatively analyzed. The validity of the prediction model was verified by comparing the proposed the entropy weight TOPSIS-BP neural network regression fitting algorithms. The results show that the evaluation results based on entropy weight TOPSIS-BP neural network model is more accurate and reliable than the existing methods. In addition, it provides theoretical suggestions for further improving the environmental innovation capability of manufacturing enterprises in China.