Text classification model of rare earths patents based on ERNE-CAB-CNN

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.