近年来，以信息技术为代表的新兴技术受到社会各界的广泛关注。然而，由于新兴技术的高度不确定性，如何有效预测并选择合适的商业化路径是目前学术界的研究难点。本文从大数据的角度出发，提出了一种基于文本分析、三元组提取法(SAO)、技术路线图等方法构建而成的“技术创新路径识别模型”，对多种异构数据反映的技术发展规律进行探索。该模型在获取研究领域主要技术点的基础上，可以对技术点间动态演化关系以及技术创新路径进行识别。在案例部分，本文以固体脂质纳米粒子（SLN）为例，研究其在医药及化妆品领域的商业化创新路径。研究表明，目前SLN有4条创新路径，其中,在化妆品产业的创新应用是未来市场开发重点。

Recently, new emerging science and technologies (NESTs) have been received extensive attention from the society. How to effectively predict the innovation pathways and select the appropriate developmental direction are core issues in current research, due to the high uncertainties in the development of NESTs. Our research deeply investigates the potential laws from the variety heterogeneous information from the perspective of big data, then relies on the methods of text analysis, SAO theory and TRM to build an identification model for gauging technology innovation pathways. In case study part, we take solid lipid nanoparticles (SLNs) as an example to deeply investigate its innovation pathways in pharmaceutical and cosmetic markets. The research results show that there are four innovation pathways in SLN domain and the most promising one is in developing cosmetic market.