The green transformation of construction enterprises (GTCEs) is an important way to develop green buildings and realize the goal of “double carbon”. The GTCEs is not only influenced by the internal characteristics of the group but also influenced by the governmental orientation and the pull of the consumer groups. This paper simultaneously considers the heterogeneity of consumer groups and construction enterprise groups, coupling the improved SIR dissemination model, complex network model, and evolutionary game model to describe the dynamic interaction process between construction enterprise groups, government, and consumer groups and to explore the evolution law of GTCEs. The results show that (1) Appropriately increase in green R&D investment by construction enterprises for higher returns, the government’s subsidy and penalty policies and a higher carbon trading price have a positive effect on the GTCEs; (2) a positive social climate, along with the government’s publicity and education, the higher technology level of construction enterprises, and the higher green cognition and lower risk perception level of consumers will strongly promote the GTCEs; and (3) a steady development of the GTCEs is guaranteed by the enterprises’ own inputs and the government’s joint measures on both the supply and demand sides. The conclusions of this study can be used as a reference for the government to formulate policies and for the green transformation and development of construction enterprises.

The accurate prediction of tunneling speed in tunnel boring machine (TBM) construction is the basis for the timely adjustment of the operating parameters of TBM equipment to ensure safe and efficient tunneling. In this paper, a multi-step prediction model of TBM tunneling speed based on the EWT-ICEEMDAN-SSA-LSTM hybrid model is proposed. Firstly, four datasets were selected under different geological conditions, and the original data were preprocessed using the binary discriminant function and the $3\sigma$ principle; secondly, the preprocessed data were decomposed using the empirical wavelet variation (EWT) to obtain several subseries and residual series; then, Intrinsic Computing Expressive Empirical Mode Decomposition With Adaptive Noise (ICEEMDAN) was used to perform further decomposition on residual sequences. Finally, several subsequences were fed into a Long Short-Term Memory (LSTM) network optimized by the Sparrow Search Algorithm (SSA) for multi-step training and prediction, and the predicted results of each subsequence were added up to obtain the final result. A comparison with existing models showed that the performance of the prediction method proposed in this paper is superior to other models. Of the four datasets, the average accuracy from the first step prediction to the fifth step prediction reached 99.06%, 98.99%, 99.07%, and 99.03%, respectively, indicating that the proposed method has high multi-step prediction performance and generalization ability. In this sense, this paper provides a reference for other projects.