Patent classification aims to assign multiple International Patent Classification (IPC) codes to a given patent. Recent methods for automatically classifying patents mainly focus on analyzing the text descriptions of patents. However, apart from the texts, each patent is also associated with some assignees, and the knowledge of their applied patents is often valuable for classification. Furthermore, the hierarchical taxonomy formulated by the IPC system provides important contextual information and enables models to leverage the correlations between IPC codes for more accurate classification. However, existing methods fail to incorporate the above aspects. In this paper, we propose an integrated framework that comprehensively considers the information on patents for patent classification. To be specific, we first present an IPC codes correlations learning module to derive their semantic representations via adaptively passing and aggregating messages within the same level and across different levels along the hierarchical taxonomy. Moreover, we design a historical application patterns learning component to incorporate the corresponding assignee's previous patents by a dual channel aggregation mechanism. Finally, we combine the contextual information of patent texts that contains the semantics of IPC codes, and assignees' sequential preferences to make predictions. Experiments on real-world datasets demonstrate the superiority of our approach over the existing methods. Besides, we present the model's ability to capture the temporal patterns of assignees and the semantic dependencies among IPC codes.

我们提出了一个综合考虑专利信息的专利分类框架，通过自适应地传递和聚合同级和不同级别的信息来推导IPC代码的语义表示；通过双通道聚合机制，设计了一个历史应用模式学习组件，以整合相应申请人的先前专利；最后，结合包含IPC代码语义和申请人的顺序偏好的专利文本的上下文信息进行预测。实证分析表明我们的方法优于现有方法，并展示了该模型捕捉申请人的时间模式和IPC代码之间的语义依赖关系的能力。

适应性分类学习与专利分类的历史模式建模