## 1 Introduction
[19] proposed a text mining approach for the causal classification of accidents using a relation graph convolutional network (R-GCN) and pre-trained BERT (Bidirectional Encoder Representation from Transformers) to automatically explore causal information in accident investigation reports. [PERSON] et al. [5] analyzed construction accident reports using the term frequency-inverse document frequency (TF-IDF) method and NLP techniques, presenting  
Figure 1: Statistics on the number of production safety accidents and deaths in China’s housing and municipal engineering industry from 2016 to 2020.  
five baseline models and an ensemble model for accident cause classification. [PERSON] et al. [20] developed a graph-based deep learning method for the automatic classification of accident reports labeled with accident types and injury types. [PERSON] et al. [21] utilized deep learning to automatically classify and predict the causes of accidents through text features and fast extraction of cause information. These studies proposed different classification methods and approaches for processing accident report texts that demonstrated good application effectiveness. However, there is still limited research on automatically classifying Chinese building accident narratives using machine learning methods. The automatic classification of accident narratives can help managers quickly understand the types of accidents. Although the automatic classification of accident narratives itself does not generate new knowledge [22], it provides a means to improve management efficiency. By knowing the specific accident types, managers can take appropriate measures. Text classification models such as SVM, CNN, and RNN can improve management efficiency but cannot extract detailed knowledge from text information [23]. Text mining techniques provide a method for obtaining detailed knowledge from textual information. It is the process of extracting previously unknown, understandable, potential, and useful patterns or knowledge from a collection of textual data [24]. [PERSON] et al. [25] combined text mining techniques with complex networks to explore the causal mechanism of coal mine accidents. [PERSON] et al. [26] developed a chemical accident case text-mining method based on Word2 vec and bidirectional LSTM for the correlation analysis and text classification of chemical accident cases. [PERSON] et al. [27] used the Latent Dirichlet Allocation (LDA) model to mine the best safety evidence regarding accident causal topics and causal factors, providing safety decision support. From these studies, it is evident that combining machine learning and text mining methods holds the potential to play a role in handling construction accident texts, and their applicability should be explored. In this context, this study aims to develop an automated categorization method for construction accident narratives in China, in order to help managers analyze and understand accident texts and to enhance their execution capabilities in terms of on-site safety management. Specifically, we developed a CNN-based text classification model to automatically classify Chinese accident narratives and then analyzed the accident narratives using the TF-IDF algorithm, employing the classified falling accidents as an example to further extract the key information. Finally, we created word clouds to visualize key information, aiding managers in understanding and analyzing accident text information. The purpose of this paper is to develop a machine learning-based automatic classification method to help managers understand and analyze accident text. Using this approach, managers can directly acquire accident experience knowledge from accident texts, enabling them to prevent accidents and respond effectively in the event of an incident.