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.