为充分挖掘专利文本中已有的解决方案和技术知识，依据发明问题解决理论（theory of inventive problem solving, TRIZ），提出了一种基于预训练语言模型的方法，将其用于面向TRIZ发明原理的中文专利分类研究中。基于整词掩码技术，使用不同数量的专利数据集（标题和摘要）对中文RoBERTa模型进一步预训练，生成特定于专利领域的RoBERTa_patent1.0和RoBERTa_patent2.0两个模型，并在此基础上添加全连接层，构建了基于RoBERTa、RoBERTa_patent1.0和RoBERTa_patent2.0的三个专利分类模型。然后使用构建的基于TRIZ发明原理的专利数据集对以上三个分类模型进行训练和测试。实验结果表明，RoBERTa_patent2.0_IP具有更高的准确率、宏查准率、宏查全率和宏F₁值，分别达到96%、95.69%、94%和94.84%，实现了基于TRIZ发明原理的中文专利文本自动分类，可以帮助设计者理解与应用TRIZ发明原理，实现产品的创新设计。

To fully explore the existing solutions and technical knowledge in patent texts, based on TRIZ (theory of inventive problem solving), a method based on pre-trained language models is proposed for Chinese patent classification research oriented towards TRIZ inventive principles. Based on WWM (whole word masking technology), the Chinese RoBERTa model was further pre-trained with different number of patent datasets (composed of title and abstract of patent), and RoBERTa_patent1.0 and RoBERTa_patent 2.0 models specific to the patent domain were generated. On this basis, a Fully Connected Layer was added to construct three patent classification models based on RoBERTa, RoBERTa_patent1.0 and RoBERTa_patent2.0. Then, the constructed patent datasets based on TRIZ inventive principle was used to train and test the above three patent classification models. The experimental results show that, RoBERTa_patent2.0_IP has higher accuracy, P_Macro, R_Macro, and F_1Macro, reaching 96%, 95.69%, 94%, and 94.84% respectively, achieving automatic classification of Chinese patent texts based on TRIZ inventive principle and helping designers understand and apply TRIZ inventive principle and achieve innovative product design.