LAUSR

Abstract This study proposes an identification model based on subject–action–object (SAO) structure semantic analysis for the potential hotspots of technology demand to address the shortcomings of technology demand mining on the basis of word frequency statistical analysis. The SAO structure is extracted using Python tools to identify the potential hotspots of technology demand, the domain dictionary and professional corpus are introduced, and the clustering of technology demand is realized by applying Word2Vec and HowNet to calculate the semantic similarity among the SAO structures. The layout of the technology demand in the different stages of the technical lifecycle is divided by constructing a technology map. The proposed model is validated as an example of the network technology demand text of the new energy and energy saving fields. Therefore, the hotspots of technology demand are the technology of new energy vehicle motor and its control system, technology of energy efficient and technology of wind power, and the new energy vehicle technology is still in the research and development (R&D) stage. Moreover, solar energy products and production equipment are still in the technical application stage. This study provides an effective method for identifying potential technology demand and based on technology lifecycle to implement the layout and visualization of demand, which will make the decision support for guiding the direction of technology R&D, optimizing the allocation of science and technology resources, and promoting the effective docking of technology supply and demand.