Radical novelty is one of the key characteristics of emerging technologies. This characteristics makes emerging technologies as a quite different from established technologies. From the perspective of radical novelty, some studies consider patents with little similarity in terms of key concepts and contents to existing patents as candidate emerging technologies. However, existing research remains in examining small-scale patents for evaluating candidate emerging technologies due to the lack of data-processing capacity—the recent rising of deep learning methods may help in this. This study, therefore, develops a novel deep learning based framework for identifying emerging technologies by combining a technological impact evaluation using patents and a social impact evaluation using website articles. Using a large scale multi-source dataset including 129,694 patents and 35,940 website articles, this paper applies the framework to investigate the case of computerized numerical control machine tool technology, through which the framework is validated. The results show that 16,131 patents out of 129,694 patents are considered as candidate emerging technologies, and 192 patents out of 16,131 patents are identified as emerging technologies through the evaluation of technology impact and social impact. This implies that these candidate emerging technologies can evolve to emerging technologies, though not all of them—we need deep learning method to scrutinize a larger scale multi-source data to identify rather a small number of potential emerging technologies. The proposed framework can also be extended to explore other disciplinary multi-source data for strategic decision support in identifying emerging technologies.

激进的新颖性是新兴技术的关键特征之一。这种特性使新兴技术与既有技术完全不同。从根本性新颖性的角度来看，一些研究认为在关键概念和内容上与现有专利不太相似的专利是候选新兴技术。但是，由于缺乏数据处理能力，现有的研究仍在审查用于评估候选新兴技术的小规模专利-近年来深度学习方法的兴起可能对此有所帮助。因此，本研究通过结合使用专利的技术影响评估和使用网站文章的社会影响评估，开发了一种新的基于深度学习的框架，用于识别新兴技术。本文使用包含129,694项专利和35,940篇网站文章的大规模多源数据集，将该框架应用于研究计算机数控机床技术的案例，从而验证了该框架。结果表明，通过评估技术影响和社会影响，在129,694项专利中有16131项被认为是候选新兴技术，在16131项专利中有192项专利被确定为新兴技术。这意味着这些候选新兴技术可以演进为新兴技术，尽管不是全部，但我们需要深度学习方法来审查更大规模的多源数据，以识别相当少量的潜在新兴技术。