数字图书馆论坛2025，Vol.21Issue(7)：31-41,11.DOI:10.3772/j.issn.1673-2286.2025.07.004
融合多维特征测度与神经网络的技术前沿识别方法
Neural Network-Based Identification of Technology Frontiers Using Multi-Dimensional Feature Measurement
摘要
Abstract
Technology frontier identification is an important approach to promoting scientific and technological innovation and supporting strategic decision-making.To address problems such as time delay and single source validation in existing methods,this paper proposes a technology frontier topic identification method that integrates multi-dimensional feature learning with feedforward neural network modeling.First,the Latent Dirichlet Allocation model is used to perform temporal topic clustering on technical texts under a sliding time window.Second,an index system is constructed with six secondary dimensions:novelty,growth,market value,influence,interdisciplinarity,and development investment.They are further summarized into three primary dimensions:technological novelty,technological growth,and topic heat index.A feedforward neural network is then used to learn topic features and quantitatively evaluate their frontier nature.Finally,the method is applied to the field of crop breeding.Both qualitative and quantitative results demonstrate the model's advantages in frontier recognition accuracy and decision-making support effectiveness.关键词
技术前沿/技术识别/主题识别/机器学习/神经网络/多维特征/作物育种Key words
Technology Frontier/Technology Identification/Topic Identification/Machine Learning/Neural Network/Multi-Dimensional Feature/Crop Breeding引用本文复制引用
廖姗姗,姜楠,康娅,孙巍,吴蕾,李周晶..融合多维特征测度与神经网络的技术前沿识别方法[J].数字图书馆论坛,2025,21(7):31-41,11.基金项目
本研究得到中国农业科学院基本科研业务费专项"农业研究热点前沿及颠覆性技术发现研究"(编号:Y2025ZZ04)、中国农业科学院信息所公益性科研院所基本科研业务费专项资金青年探索项目"设施农业领域共性技术扩散路径研究"(编号:JBYW-AII-2024-31)资助. （编号:Y2025ZZ04）
