摘要
为能够快速准确地对高校发明专利质量进行评估,充分考虑专利的文本属性和结构化数据信息,基于深度学习技术构建基于GRU-Attention机制的高校发明专利质量评估模型,并通过引入衡量高校发明专利质量的关键指标及权重信息对初始模型进行改进。经验证,结合专家知识改进后的评估模型在稳定性和准确率上有明显优势,能够为高校发明专利质量评估提供有效的理论和实践支撑。
In order to be able to quickly and accurately evaluate the quality of university invention patents,this paper takes full account of the text attribute and structured data information of patents,constructs a university invention patent quality evaluation model based on GRU-attention mechanism based on deep learning technology,and improves the initial model by introducing the key indicators and weight information to measure the quality of university invention patents.It has been verified that the improved evaluation model combined with expert knowledge has obvious advantages in stability and accuracy,which can provide effective theoretical and practical support for the quality evaluation of invention patents in universities.
作者
谢祥
马晓雅
卢彤
张婧
Xie Xiang;Ma Xiaoya;Lu Tong;Zhang Jing(School of Economics and Management,Beijing Jiaotong University,Beijing 100044,China;China Railway Material Co.,Ltd,Beijing 100044,China)
出处
《科技管理研究》
CSSCI
北大核心
2021年第7期79-85,共7页
Science and Technology Management Research
基金
国家自然科学基金应急项目“面向2035的前沿新材料技术路线图研究”(L1824045)
中国铁路集团公司科技研究开发计划重点课题“财会信息系统整合优化研究”(N2018Z008)。
