Research on Classification of Coal Mine Hidden Danger Texts Based on TextCNN-Attention-BiLSTM Fusion Model
In order to achieve quick and accurate classification of a large number of coal mine hazard texts,and timely un-derstand the safety situation for effective management,first,multiple coal mine hidden danger databases from the website of safety library were selected as experimental data sources,the coal mine hidden danger texts were preprocessed,including noise words removal,word segmentation,and word vector representation,etc.Then,the TextCNN(text convolutional neural network)was used to perform convolution operations on the texts,extracting feature representations of different sizes.The BiLSTM(bi-direc-tional long short-term memory)model was utilized to sequentially model the obtained feature vectors.Combined with the atten-tion mechanism(Attention),the model can better focus on key information in the texts and capture the global semantic informa-tion of the texts.Finally,a multi-label classifier in the fully connected layer was used to predict the categories of hidden dan-gers in the texts.Experimental results showed that the fused TextCNN-Attention-BiLSTM model achieves over 92%accuracy,precision,recall,and F1 value,providing a more accurate and effective solution for coal mine hidden danger text classification.It is of great significance for optimizing coal mine safety management.
NETL
NSTL
万方数据
维普
