With the advancement of science and the emergence of new technologies, technology opportunities analysis has attracted increasing attention from both society and academia. This study proposes a hybrid approach to integrate topic modeling, semantic SAO analysis, machine learning, and expert judgment, identifying technological topics and potential development opportunities. The systematical methodology is applied to analyze a set of 9,883 Derwent Innovation Index (DII) patents related to the dye-sensitized solar cell to present its potential contribution of technical intelligence for R&D management. Also, how the approach is validated and optimized is illustrated. The main contributions of this paper are two-fold. First, an optimized topic extraction model with high accuracy is constructed, considering both the patent classification codes and term location. Second, we integrate the topic modeling, SAO technique, and machine learning to explore semantic relationships among technological topics represented as a suite of terms. The methodology overcomes some drawbacks of the current studies. It can be used as a powerful tool for technological opportunities analysis.

随着科学的进步和新技术的出现，技术机会分析越来越受到社会和学术界的关注。本研究提出了一种混合方法来整合主题建模、语义 SAO 分析、机器学习和专家判断，识别技术主题和潜在的发展机会。该系统方法用于分析与染料敏化太阳能电池相关的 9,883 项德温特创新指数 (DII) 专利，以展示其技术智能对研发管理的潜在贡献。此外，还说明了如何验证和优化该方法。本文的主要贡献有两个方面。首先，构建一个优化的高精度主题提取模型，考虑专利分类代码和术语位置。其次，我们整合了主题建模、SAO 技术和机器学习，以探索表示为一组术语的技术主题之间的语义关系。该方法克服了当前研究的一些缺点。它可以用作技术机会分析的强大工具。