## 4 Discussion  
With the continuous advancement in natural language processing (NLP) technology, machine learning methods have found extensive applications in accident management. Although significant progress has been made in analyzing construction accident texts using machine learning methods in foreign countries, the application of intelligent methods based on machine learning to construction accidents in China is still relatively limited. This is primarily attributed to the linguistic characteristics, the cultural background differences in the Chinese context, and the lack of large-scale annotated Chinese construction accident datasets, which collectively pose challenges to the application of machine learning methods. In order to better meet the analytical needs of Chinese construction accident texts and to enhance the accuracy and efficiency of accident analysis, this study has developed a deep learning-based text classification model. The model adopts Convolutional Neural Network (CNN) technology to extract text features and automatically recognize accident types. Additionally, the model integrates the TF-IDF algorithm from text mining and the visualization technique of word clouds to facilitate the extraction and comprehension of crucial accident-related information. The significance of this research is demonstrated in several aspects. Firstly, through empirical evidence, the feasibility of various machine learning methods in processing Chinese accident texts has been established. Specifically, the deep learning CNN model efficiently and accurately identifies accident types within Chinese construction accident narratives, thereby helping management personnel to analyze incident texts and eliminating the need for the manual analysis of accident narratives. Secondly, the TF-IDF text mining method enables the extraction of essential knowledge from construction accident narratives. Diverging from previous research that primarily focused on identifying key accident causes, this study emphasizes critical accident-prone areas and operational activities, thereby providing a novel perspective for on-site management personnel. Lastly, the amalgamation of deep learning and text mining techniques not only holds potential in the domain of construction accidents but is also applicable to other accident-prone sectors such as chemical, power, and transportation industries. These fields also involve substantial unstructured or semi-structured textual data, encompassing accident reports, descriptions, and records. Similar to the construction accident domain, these sectors need to extract key information, identify accident types, and uncover latent issues from massive textual data. In the future, applying deep learning and text mining methodologies to these sectors is anticipated to further enhance the analytical capabilities and management efficiency of accident-related data, thereby providing more comprehensive and precise support for safety incident management. Figure 7: Fall accident word cloud map of the top 20 words. (**a**) Chinese word cloud and (**b**) English word cloud. It is worth noting that the CNN model proposed in this study falls under the category of supervised learning and involves the labeling of only one accident category during data preprocessing. However, incidents often involve multiple accident types. Therefore, future research could focus on developing classifiers capable of recognizing various accident types, enabling a comprehensive evaluation of incident types when accidents occur. Furthermore, this study exclusively concentrates on the automatic classification of five typical accident types within the Chinese construction industry, although the spectrum of actual accident types is far more diverse. As such, future endeavors should be directed towards creating more comprehensive classifiers, encompassing a wider range of accident types to assess model performance. Moreover, the introduction of more diverse data sources will enhance the effectiveness of the model.