Research on Investment Estimation of Prefabricated Buildings Based on Genetic Algorithm Optimization Neural Network

At present, China’s prefabricated buildings have entered a period of comprehensive development. Starting from the investment decision-making stage of construction projects, this paper analyses the characteristics of prefabricated investment estimation and the relevant literature on the characteristics of prefabricated construction projects, uses the rough set attribute reduction algorithm to screen the key engineering characteristic factors, and establishes a BP neural network model optimized by genetic algorithm to estimate and analyze the investment of completed prefabricated construction projects. The results of the study show that the accuracy of the improved BP neural network prediction model is better than that of the standard BP neural network prediction model, and the results are more stable, which provides a more scientific and effective method for the investment estimation of prefabricated buildings projects.