基于机器学习的颠覆性技术弱信号识别模型研究

Weak Signal Identification Model for Disruptive Technologies Based on Machine Learning

【目的】基于机器学习构建颠覆性技术弱信号识别模型,发现早期的颠覆性技术并探究其对现有主流技术的未来颠覆潜力。【方法】通过归纳颠覆性技术弱信号的核心特征,设计基于专利引证类别的颠覆性指数DI-P,构建历史颠覆性技术语料,设计基于机器学习的颠覆性技术弱信号识别模型,选取逻辑回归、高斯朴素贝叶斯、随机梯度下降、梯度提升树和随机森林等多个机器学习模型综合预测,并通过链路预测探究颠覆性技术弱信号的未来颠覆路径。【结果】在储氢领域开展实证分析,构建基于引证类别的颠覆性指数DI-P获取历史颠覆性技术语料,其准确率与AUC值均优于RDI与DI。通过对比颠覆性技术弱信号与高价值专利,能够从成本、效率及安全性等角度发现其未来可能的颠覆路径。【局限】实证领域相对单一,数据源局限于专利数据与战略规划,预测模型准确率有限。【结论】通过结合机器学习模型与链路预测方法,能够精准、细粒度地识别颠覆性技术弱信号及其颠覆路径。

[Objective]This paper constructs a weak signal identification model for disruptive technologies based on machine learning.It aims to discover early-stage disruptive technologies and explore their disruptive potential to existing mainstream technologies.[Methods]By summarizing the core characteristics of disruptive technology’s weak signals,we designed a Disruptive Index-Patent(DI-P)based on the patent citation categories.We also constructed historical disruptive technology corpora and designed a weak signal identification model for disruptive technologies based on machine learning.Machine learning models such as Logistic Regression,Gaussian Naive Bayes,Stochastic Gradient Descent,Gradient Boosting Trees,and Random Forests were selected for comprehensive prediction.Finally,we explored the future disruptive paths of technology’s weak signals through link prediction.[Results]We conducted an empirical analysis in hydrogen storage and used the DI-P based on citation categories to obtain historical disruptive technical corpora.Its accuracy and AUC values were better than RDI and DI.By comparing the weak signals of disruptive technologies with high-value patents,we can identify potential future disruptive paths from the perspectives of cost,efficiency,and security.[Limitations]The empirical field is relatively single,data sources are limited to patent data and strategic planning,and the prediction model has limited accuracy.[Conclusions]By combining machine learning models with link prediction methods,we can identify signals of disruptive technologies and their disruption paths with precision and fine granularity.