Technology convergence represents a significant mode of technological innovation that is widely prevalent across various industries. This innovative approach integrates multiple technologies to develop new integrated solutions, thereby fostering a competitive advantage for enterprises . Anticipating future potential technology convergence is of paramount importance for businesses. However, previous research has predominantly relied on the topological information of convergence networks, overlooking the nodal attributes and inter-nodal relationships that have an impact on the emergence of technology convergence. To enhance existing studies, this paper employs three types of features: node attributes and inter-node relationships based on the drivers of technology convergence, along with link prediction similarity indices . Additionally, we utilize Graph Convolutional Neural Network (GCN) for node embedding to leverage node attributes. Machine learning models are utilized for link prediction based on these features to identify potential technology opportunities. To guide research and development (R&D) efforts, we recommend high-value patents for each node using entropy weighting across five metrics that objectively quantify patent value, and transform patent abstracts into vectors using Doc2Vec. Patents with high similarity in abstract text between nodes are utilized to extract technical solutions and fuse ideas for technology convergence. A case study is conducted within the autonomous driving industry, leveraging comprehensive information including node attributes, inter-node relationships, and topology-based similarities to identify technology opportunities and guide the generation of R&D ideas through the convergence of technical solutions.

The Collective Intelligent Design Ecosystem is a dynamic ecosystem founded on an online design platform that leverages collective intelligence to support the creation of novel products. The system's primary components are its users and designers. Maintaining the system's sustainability requires expanding the scale of the designer and user populations as it evolves to stabilize. However, the unity of ecological interactions between various populations is fragmented in contemporary studies of population-scale evolution, and the parameterization of evolutionary models is illogical. To overcome this gap, this research provides a population evolution model of collective intelligent design incorporating participants' intra- and interspecific ecological connections. The model's validity is verified by the evolutionary simulation of 110 designers and 5990 users of China's largest collective intelligence design platform, the Zhubajie platform, and illuminating conclusions are in turn drawn from this simulation. First, the designer's influence on the user is greater than the user's impact on the designer. Second, keeping current members engaged is more crucial to the system's viability than luring in new ones. Third, fostering collaboration among designers while retaining user competitiveness can promote system growth. Fourth, decreasing the reliance between particular designers and users might hasten the system's evolution.