MPMFC: A Traditional Chinese Medicine Patent Classification Model Integrating Network Neighborhood Structural Features and Patent Semantic Features

[Objective] To solve the problem of low accuracy in classification models for Traditional Chinese Medicine (TCM) patents due to the complexity of TCM and insufficient extracted information on the characteristics of TCM patents. [Methods] We proposed a classification model for TCM patents called MPMFC (Medicine Patent Multi-feature Fusion Classifier). Firstly, we constructed a TCM patent similarity network based on the similarity information of the patent core fields. Then, we used the Node2Vec algorithm to capture the neighborhood structure information of potential patents from the global structure of the TCM patent similarity network, which was mapped to low-dimensional vectors as additional features. Finally, the attention mechanism was utilized to fuse the patent semantic feature vector pre-trained by RoBERTa-Tiny with their corresponding supplementary features to classify TCM patents automatically. [Results] We examined the MPMFC model on a corpus of 7, 000 TCM patents. It achieved the accuracy, recall, and F1 values of 0.8436, 0.8017, and 0.822 1, respectively, which were 1.58%, 2.59%, and 2.11% higher than the baseline classification model. [Limitations] The weight allocation when constructing the similarity network of TCM patents has subjectivity issues. There may be some classification errors when Non-TCM researchers label patents. [Conclusions] The MPMFC model can acquire and learn more comprehensive feature representations from multiple perspectives during TCM patent classification, improving classification accuracy. © 2023 Data Analysis and Knowledge Discovery. All rights reserved.