Prediction and Analysis of Cement Price Index Based on LSTM Neural Network Model Under the “Dual Carbon” Goal

With the environmental protection, green development is increasingly imminent and deeply rooted in the hearts of the people, China proposed to achieve "carbon neutrality" by 2030, to achieve "carbon peak" by 2060, this grand goal has a significant impact on many industries, especially the energy industry. In this context, the cement price index is an important basis for measuring the supply and demand of cement, and its fluctuations plays an important role in data support for social production regulation and control. The traditional econometric model, especially the linear econometric model, has poor prediction effect due to the difficulty of considering various macro and micro factors, while the neural network model can effectively deal with the nonlinear samples of cement value index by constructing a network structure composed of multi-layer neurons connected to each other. The evaluation indexes in the comparison with GRU neural network show that the LSTM neural network model can effectively improve the prediction accuracy. This study provides a valuable reference for the prediction of the future trend of cement price index and the formulation of related policies.