Deep Ensemble Learning with CNNs, DPCNNs, and LSTMs for Construction Cost Classification

With the advancement of cost informationization in construction, the automatic classification of building project costs has become a key step to improving management efficiency. Traditional rule-based or manual methods are insufficient to handle increasingly complex engineering texts. To address this issue, this study proposes a deep learning framework that integrates Convolutional Neural Networks (CNNs), Deep Pyramid Convolutional Neural Networks (DPCNNs), and Long Short-Term Memory networks (LSTMs). A standardized dataset of 12,838 records was constructed based on expert annotation. Six baseline models were trained under both character-level and word-level tokenization, and their predictions were combined through a majority voting strategy. Experimental results show that the ensemble model achieved an accuracy of 97.59% on the test set, outperforming single models, with character-level tokenization performing better. The findings confirm the effectiveness of model ensembling in enhancing classification accuracy and robustness, providing a feasible solution for intelligent text classification in cost management, and offering practical reference for digitalization and intelligent applications.