Abstract: Organized industry-university-research (IUR) collaboration is crucial for leveraging China's new national system advantages, enhancing the overall efficacy of the national innovation system, and achieving deep integration of industry, university, and research. Identifying collaboration topics is paramount for high-quality, efficient synergy in such collaborations and constitutes a primary challenge. This paper proposes a set of identification methods for IUR collaboration topic pairs based on complex network analysis and deep learning algorithms. Firstly, this paper analyzes the concept of "Organized IUR Synergy Innovation" and related topic features, proposes that the topic of IUR collaboration should be high-value and strong-relevance. Secondly, a bi-layer network of "science keywords-universities and research institutions" and a bi-layer network of "technology keywords-enterprises" are constructed based on paper data and patent data. The SciBERT model is used to construct a semantic network of science keywords and a semantic network of technology keywords. The Node2vec-based link prediction model is used to generate the future semantic network of science keywords and the semantic network of technology keywords. Then, the five indicators of novelty, fundamentality, width, growth, and foresight are measured using methods of complex network topology analysis, community discovery, and machine learning, to identify high-value scientific and technological topics. Finally, the correlation between scientific and technological topics is calculated using the improved semantic similarity index, SimDoc. This enables the selection of “science-technology topic pairs” that exhibit high potential for collaboration across IUR. In this paper, an empirical study is conducted using paper and patent data in the field of “artificial intelligence (AI)” from 2018 to 2022 to validate the research method and results. This study identifies 20 high-value scientific and technological topics in the field of AI. The results reveal that scientific topics focused more on basic theoretical research such as neural network training algorithms, human-computer interaction methods, and large model training algorithms, while technological topics focused more on application research such as computer vision technologies, computing devices, and big data processing techniques. The correlation analysis results between scientific and technological topics show that the overall correlation is above 0.5, and there are 22 science-technology topic pairs with scores of 0.9 or above, indicating that organized IUR synergy innovation in the AI field has a good foundation. The primary theoretical contributions are as follows. First, integrating the concept of "organization" into the design of an efficient IUR collaborative system, deepening the theoretical research on IUR synergy innovation. Second, expanding the application of complex network analysis and deep learning algorithms in the field of IUR synergy innovation research. Third, the integration of machine learning and text semantic analysis methods provides a useful supplement for deep mining of the intrinsic relationships between IUR cooperation topics. Fourth, enriching the theoretical and methodological research in areas such as optimizing the allocation of innovative resources and transforming scientific and technological achievements. This study can provide important decision-making support for government departments to guide enterprises, universities, and research institutes to carry out high-level IUR collaborative innovation in an organized manner, and play an important quantitative supporting role in promoting the deep integration of the innovation chain and industrial chain in practice.