A novel heuristic approach for sustainable social and economic development based on green computing technology and big data

Purpose: Without damaging and consuming natural resources, green computing technology can meet the needs of society for a long time. This paper discusses how to realize the sustainable development of social economy through the innovation of green computing technology. Design/methodology/approach: For the green computing technology and sustainable social and economic development problems, it builds back propagation (BP) neural network model and analyzes the topological structure of the network model as well as the impact of the training errors allowed by the network on its performance. Findings: By optimizing the number of input nodes, the number of hidden nodes and the target value, the genetic algorithm (GA) can get the optimal neural network model. The simulation experiment proves that the proposed model is effective. Originality/value: It can not only reduce the possibility of falling into local optimum, but also optimize the initial weights and thresholds of BP neural network and further improve the stability and test effect of BP neural network model.