Evaluation Method of Industrial Efficiency of Green Manufacturing Enterprises Based on Machine Learning

Abstract

The cultural construction in the process of industrialization is intertwined with the culture required by the development of green manufacturing industry, which has become the growth point of economic construction and the new trend of economic development. Efficiency is the basis for the development of various industries. If we do not improve efficiency, industrial development will cause waste of resources and environmental pollution. Therefore, this study proposes a new evaluation method of green industrial manufacturing efficiency. The proposed method suggests cultural enterprises. In addition, this study examines the cultural green manufacturing industry productivity of cultural enterprises and combines it with machine learning. The excellent performance of neural network in prediction makes it possible to predict the efficiency of green manufacturing industry of cultural enterprises. Genetic algorithm is also proposed to optimize BP network. This algorithm is easy to operate and requires few parameters. In the process of finding the optimal solution, the optimal individual in the group can be used to control the iterative process. The particle swarm optimization algorithm is improved and combined with genetic algorithm to get an improved hybrid algorithm. BP network is optimized, and an improved BP network prediction model is established to evaluate the efficiency of green manufacturing industry of cultural enterprises. A large number of experiments have proved the effectiveness and reliability of this method. Separate simulations and results are presented to verify the effectiveness of the proposed model.