In the text classification problem, the efficiency of manual labelling is very low, and professionals familiar with the research field are needed to carry out this work. In order to improve the efficiency of text data labelling, a semi-automatic paper category labelling method is proposed. Firstly, the vector representation of paper abstracts is derived by the combination of word2vec and TF-IDF; then the K-means algorithm is used to complete text clustering; K classification models are constructed through the $L_1$-LR binary classification model; For each binary classification model, the word corresponding to the coefficient with large absolute weight value is selected as the subject word. Finally, the label of each category is determined according to the subject word. The proposed semi-automatic paper category labelling method greatly improves the efficiency of text labelling.