Abstract: Natural language processing(NLP) is an important research direction in the field of artificial intelligence and machine learning,which aims to use computer technology to analyze,understand,and process natural language.One of the main research areas in NLP is to obtain information from textual content and automatically classify and label textual content based on a certain labeling system or standard.Compared to single-label text classification,multi-label text classification has the characteristic that a data element belongs to multiple labels,which makes it more difficult to obtain multiple categories of data features from textual information.Hierarchical classification of multi-label texts isa special category,whichdivides the information contained in the text into different category labeling systems,and each category labeling system has an interdependent hierarchical relationship.Therefore,the use of the hierarchical relationship in the internal labeling system to more accurately classify the text into corresponding labels becomes the key to solving the problem.To this end,this paper proposes a hierarchical classification algorithm for multi-label texts based on the fusion of parallel convolutional network information.First,the algorithm uses the BERT model for word integration in textual information,then it enhances the semantic features of textual information using a self-attention mechanism and extracts the features of textual data using different convolutional kernels.The multi-faceted semantic information of the text is more effectively used for the task of a hierarchical classification of multi-label texts by using a threshold-controlled tree structure to establish inter-node relationships between higher and lower bits.The results obtained on the Kanshan-Cup public dataset and the CI enterprise information dataset show that the algorithm outperforms TextCNN,TextRNN,FastTex and other comparative models in three evaluation measures,namely macro-precision,macro-recall,and micro F1 value,and has a better cascade multi-label text classification effect.

Key words: Hierarchical multi-label text classification, Pre-training model, Attention mechanism, Convolutional neural network, Tree structure