## 1 Introduction  
Investment estimation is one of the important contents of the preliminary work of engineering construction, and is an important basis for the feasibility study and investment decision-making [1]. At this stage of China's prefabricated buildings standardization, integration degree is insufficient, thus resulting in difficult to reduce the construction cost greatly restricts its benign development. In order to further promote the development of prefabricated construction projects and meet the reinvestment estimation needs of prefabricated construction projects, China's Ministry of Housing and Urban-Rural Development issued the \"Investment Estimation Indicators for Prefabricated Construction Projects\" in 2023. Therefore, how to accurately control the investment estimation of prefabricated building projects is gradually becoming the focus of research on prefabricated building management. Due to the different production and construction methods between pre-fabricated buildings and traditional buildings, using traditional methods such as capital turnover rate method, production capacity index method, and comprehensive index investment estimation method to estimate prefabricated buildings is inevitably unreasonable, and digital technology must be introduced for fast and accurate estimation. In the era of coordinated development of intelligent construction and building industrialization, the innovation and iteration speed of digital technology in China has significantly accelerated. Digital technology is also integrating into various fields of our lives with new concepts, new formats, and new models, and artificial intelligence is one of them. With the rise in artificial intelligence, machine learning models and deep learning techniques have gradually attracted the attention of scholars. As deep learning usually requires a large amount of data for training to fully utilize its advantages, this article selects machine learning for regression prediction of prefabricated building investment estimation. Meanwhile, considering the nonlinear relationship between the characteristic parameters and output indicators of the selected project in this study, a nonlinear model will be selected for prediction in this paper. Nonlinear models in machine learning typically include methods such as XGBoost, decision trees, random forests, support vector machines, and neural networks. Among them, neural networks, as an important direction of artificial intelligence, have been introduced into the field of engineering investment estimation by many scholars. Based on previous research, this paper will use genetic algorithms to optimize the BP neural network method for investment estimation of prefabricated buildings, solving the limitations of decision tree instability, large memory consumption of random forests, and poor interpretability of support vector machines. At the same time, it enhances global search ability, avoids local optima, avoids overfitting, and improves model training efficiency. In the current era of machine learning, scholars have also begun to use this method to study investment estimation in the engineering field. In addition, scholars' research on genetic algorithms is constantly deepening. [PERSON], [PERSON], and others proposed a prefabricated concrete building investment estimation model based on the XGBoost machine learning algorithm. The construction feature indicators of prefabricated building investment estimation were extracted using the construction project cost significance theory and the analytic hierarchy process. Then, the XGBoost machine learning algorithm prefabricated building investment estimation model was constructed to quantify confidence and prediction uncertainty. Compared with traditional machine learning methods, such as support vector machines, backpropagation neural networks, and random forests, it was found that XGBoost has better generalization and interpretability [2]. [PERSON], [PERSON], and [PERSON], [PERSON], used VensimPLE software to establish a system dynamics model.