中圖分類號：TP3 文獻(xiàn)標(biāo)志碼：A DOI: 10.16157/j.issn.0258-7998.245583 中文引用格式： 廖列法，石利嬌. 基于ERNIE-CAB-CNN的稀土專利文本分類模型[J]. 電子技術(shù)應(yīng)用,，2025,，51(1)：18-24. 英文引用格式： Liao Liefa，Shi Lijiao. Text classification model of rare earths patents based on ERNE-CAB-CNN[J]. Application of Electronic Technique,，2025,，51(1)：18-24.
Text classification model of rare earths patents based on ERNE-CAB-CNN
Liao Liefa，Shi Lijiao
School of Information Engineering,， Jiangxi University of Science and Technology
Abstract： In view of the strong specialization of rare earth patents and the shortcomings of existing classification methods, this paper proposes a Category Attention Block (CAB) for text classification in view of the wide application of category attention in the field of computer vision. Combined with ERNIE and Convolutional Neural Network (CNN), an innovative model ERNE-CAB-CNN for rare earth patent text classification is constructed. The model uses ERNIE to vectorize the patent text, and obtains the vector representation with richer semantic information. Then, it assigns higher weights to the key features of each category in the text through CAB, so that the model can distinguish different types of features more accurately. Finally, CNN is used to further extract other key local features in the text, and the resulting text vector representation is used for classification. Through the official website of Patsnap patent database, rare earth patent data are retrieved and downloaded to build a dataset for experiments. The experimental results show that the precision rate, accuracy rate and F1 score of the rare earths patent text classification model based on ERNE-CAB-CNN on the test set are 82.68%, 83.2% and 82.06%, respectively, achieving a good classification effect.
Key words : rare earth patent classification,；text classification；category attention,；ERNIE,；CNN；feature extraction
