Multi-label and multi-level chinese patent classification based on semantic matching

As the "14th Five-Year Plan" proposes to protect and encourage more high-value patents in the country, the number of innovative patent applications across disciplines and fields has surged, and the demand for automatic patent classification methods to assist manual classification is increasing.At present, the Chinese patent classification is mainly determined by the examiner′s manual matching with the international patent classification system table according to the patent content submitted. The manual efficiency is low, while the existing automatic classification methods mainly extract the text structure features and semantic features from the patents, and directly match the two features with the classification labels of the international patent classification system table.The existing classification methods do not take into account the semantic information of the interpretation text of the classification labels in the international patent classification table, which easily leads to fuzzy classification. Therefore, this paper transforms the traditional text classification problem into a text matching problem based on semantic features.Propose a multi-label and multi-level Chinese patent classification method based on semantic matching to realize the multi-label and multi-level classification task of patent text: extract the semantic features of each label at each level (department, major category, sub-category, major group, and group) from the international patent classification table, and extract it from public patents The semantic features of the text, and the semantic matching between the two, so as to achieve the purpose of automatic classification. A model comparison experiment was conducted on the same data set, and the results showed that the patent classification method based on semantic matching proposed in this paper can achieve better results.