# Relational Graph Convolutional Network for Text-Mining-Based Accident Causal Classification
###### Abstract
According to the different ways of constructing models, existing research can be divided into the methods based on statistics and machine learning [16; 17; 18; 19] and the methods based on deep neural networks [20; 21; 22; 23]. The methods based on statistics and machine learning are mainly utilized to manually determine a series of text features, such as the term frequency, keyword search, N-grams [24], etc. These methods transform the original unstructured text data into structured feature vectors by artificially determining some features that can represent the key information of the document and, at the same time, create new features based on the existing data. The authors of [25] adopted a variety of machine learning and text mining methods, such as support vector machine (SVM) and Naive Bayes (NB). By combining them into a more powerful learning algorithm through ensemble learning methods, results showing an accuracy of 1.0, a recall rate of 0.96 and a F1-score of 0.96 were obtained. [PERSON] et al. [26] utilized five baseline models to classify the cause of the accident, including SVM, linear regression (LR), K-nearest neighbor (KNN), decision tree (DT) and NB, and the weight of each classifier in the integrated model was optimized by the sequential quadratic programming (SQP) algorithm. In general, the classification results of simple statistical and machine learning methods, such as keyword search or SVM, largely depend on the quality of feature selection and have a high misidentification rate in the analysis of accident causes [27]. The methods based on deep neural networks usually map the terms in the text to the word vector space, process the word vector and classify it with the help of the structure of the neural network, which has gradually become the mainstream of sequential data processing in recent years. [PERSON] et al. [20] exploited Word2 Vec to skip the gram model to learn the word embedding from the corpus of a specific domain and embedded the learned words into the mixed structured deep neural network for accident report classification. [PERSON] et al. [22] proposed a latent Dirichlet assignment (LDA) algorithm model to identify risk topics and utilized convolutional neural networks (CNNs) to automatically classify hazards. Meanwhile, a word co-occurrence network (WCN) was generated to determine the relationship between hazards and word cloud (WC) technology was used for the quantitative analysis of keywords to provide a visual overview of hazard accident records. [PERSON] et al. [28] employed deep learning methods and powerful word embedding (such as Word2 Vec and GloVe) to classify accident cause values in the main cause field using text in the narrative. The results show that these methods not only can accurately classify the causes of accidents according to the report description, but can also find the important inconsistencies in the accident report. A deep neural network is essentially a polynomial regression model, which is better characterized by the stacking of multi-layer neural units than a shallow classifier such as SVM [29]. This superb characteristic also enables the model to have the ability of processing text data and implement accident causal classification. However, the existing methods either rely on large corpus and data preprocessing methods, which are cumbersome, or extract text information based on bidirectional encoder representation from transformers (BERT) [30], but the computational cost is extremely high. These shortcomings make it still a great challenge to automatically analyze accident investigation reports and extract the information therein. To address the aforementioned problems, this study proposes a text-mining-based accident causal classification method based on a relational graph convolutional network (R-GCN) and pre-trained BERT.