Activity Prediction of Construction Enterprise based on Semi-supervised Learning

In this paper, the idea of semi-supervised learning is used to propose a method for predicting the activity of construction enterprises based on the SOM-GMM-RF model. This prediction method can help enterprises understand their activity and operation status and make reasonable decisions. The goal is to address the issue that the cost of data annotation is too high in this study, and the prediction effect of the classification model is unsuitable under the condition of only a small amount of annotated data. According to the experimental data, the random forest model trained using the methodology in this study achieves an accuracy of 93.33%, which is 4.29% greater than the random forest model trained with a labeled training set. Additionally, the model has the best prediction effect in the experiment compared to other classification algorithms. The experiment reveals that the method in this paper can effectively use unlabeled data to improve the prediction effect of the model, even when only a small amount of data is labeled. This finding is important for research on predicting construction enterprise activity.