Abstract: Technology opportunity identification using the concept of the gap between science and technology has attracted attention in the academic community. However, there is limited research that considers the exploration of deep semantic features in scientific and technical literature to enhance the interpretability of technology opportunity identification results. This study proposes a novel method that combines SAO (Subject-Action-Object) semantic structure with BERTopic to address this gap. The proposed method utilizes a deep pre-trained language model to establish a correlation matrix between scientific and technical literature and SAO-annotated technical terms. By leveraging the output of the language model, the method captures the underlying semantic relationships between the literature and technical terms, enabling a more comprehensive analysis. In a sequential manner, the method focuses on identifying the gap between science and technology. For scientific literature, a combination of OPTICS (Ordering Points to Identify the Clustering Structure) and DBSCAN (Density—Based Spatial Clustering of Application with Noise) algorithms is employed to extract technological topics. The advantage of this combined algorithm is its ability to perform outlier detection while clustering topics, without the need for complex parameter tuning. This feature contributes to its efficiency, making it a practical and effective approach for technology opportunity identification. In the context of extracting technological topics from patent literature, the study employs the GTM (Generative Topographic Mapping) Patent Map method to effectively identify and map technology vacuums. In the final stage, the method employs TF-IDF (Term Frequency–Inverse Document Frequency) cosine similarity to identify and filter scientifically feasible gaps between science and technology as potential technology opportunities. To validate the effectiveness of the proposed method, the study applies it to the domain of solar thermal power generation technology. The scientific and technical literature from 2013 to 2022 serves as an empirical case. The results demonstrate the applicability of the method, with scientific topics clustered into three classes and 22 outliers identified. In addition, the analysis of patent literature reveals four technology vacuums that effectively reflect areas with valuable information. By screening the gap between science and technology, the study identifies four potential technology opportunities, including coating technology based on metals, ceramics, and powders, vacuum powder insulation methods, tube and plate collector devices, molten salt materials based on chlorides and nitrates. Importantly, these opportunities align with the actual application of technologies in the field. In summary, this research enriches and extends the existing methods for technology opportunity identification by combining SAO semantic structure and BERTopic, as well as integrating the methods of OPTICS and DBSCAN. The combination of these approaches enhances the interpretability and efficiency of technology opportunity identification. The findings of the study provide valuable insights for professionals in relevant technology fields, enabling them to assess technology development trends and identify potential areas for innovation. In conclusion, the proposed method improves interpretability while maintaining efficiency. The empirical case in solar thermal power generation technology validates the effectiveness of the approach, leading to the identification of four potential technology opportunities. This research contributes to the field by providing a comprehensive and efficient method for technology opportunity identification, benefiting professionals in technology-related domains.