Abstract:  The development of emerging technologies not only brings innovation and market opportunities, but also brings new challenges for science and technology governance. The theory of Collingridge dilemma suggests that the social governance of technology is hindered because when negative externalities of a specific technology emerge, it has become deeply entangled with human society. Therefore, how to identify or foresee the potential risks of emerging technologies in the early or initial stages is of great significance for the formulation and improvement of relevant science and technology policies. Identifying the early risks of emerging technologies is a scientific problem due to the sparse and difficult-to-identify signals. Based on the proposed three dimensions of researcher knowledge, enterprise cognition and individual perception, this paper used academic papers, patents, enterprise annual reports, and social media data to construct a quantitative framework for identifying emerging technology risks. An empirical test was conducted by using the example of autonomous driving technologies. The theoretical and empirical analyses showed that the integrated analysis framework based on deep learning and multi-source data can further mine potential risk signals of emerging technologies, thus providing more diverse decision-making references for science and technology risk governance. Among various deep learning methods, the Siamese-BERT twin network model has better risk signal mining effects for emerging technologies. Regarding autonomous driving technology, the empirical analysis showed that potential risks involving many different fields require attention from relevant policy and supervision departments. This paper has presented a theoretical and quantitative analysis framework based on deep learning for emerging technology risk analysis, which has further enriched the current theoretical and methodological system of emerging technology management from the perspectives of knowledge management and text semantic mining, and it will provide a new perspective and tools for potential risk assessment in the early stages of emerging technology evolution.

Key words: emerging technology risk, risk identification, multi-source data fusion, deep learning, knowledge base