Green Innovation Ecology Improvement: Enterprise Green Innovation Capability Evaluation Model based on Improved BP Neural Network

Yanji Li
School of Management, Wuhan University of Technology, Wuhan 430070, China

Abstract:

With the continuous advancement of global green finance and sustainable development concepts, the evaluation of corporate green innovation capabilities has become an important issue in promoting the development of the green economy. This paper proposes a corporate green innovation capability evaluation model based on an improved BP neural network (BPNN), and optimizes the performance of the model by introducing variational mode decomposition (VMD) and sparrow search algorithm (SSA). In the process of model construction, VMD is used to denoise the data to improve the data quality and feature extraction ability; while SSA overcomes the problem that traditional BP neural networks are prone to fall into local optimal solutions by optimizing the weights and biases of the neural network. Experimental verification shows that the model based on VMD-SSA-BPNN shows better performance than the traditional BPNN model in evaluating corporate green innovation capabilities, especially in error indicators such as MAE, MAPE and MSE, which have been significantly optimized. This paper also analyzes the advantages of the model in practical applications, pointing out that it has strong adaptability and scalability, and can be widely used in green finance, environmental protection, intelligent manufacturing and other fields. Finally, this paper discusses future research directions, including improvements in feature selection and fusion, real-time update and dynamic evaluation of models, and multi-model integration. In general, this study provides new ideas and methods for the evaluation of corporate green innovation capabilities, and provides a scientific basis for the formulation of relevant policies and the development of the green economy.