Science and technology are becoming increasingly collaborative. This paper aims to explore the factors and mechanisms that impact the dynamic changes of collaborative innovation networks. We consider both collaborative interactions of organizations and their knowledge element exchanges to reveal how social and knowledge network embeddedness affects the collaboration dynamics. Knowledge elements are extracted to present the core concepts of scientific and technical information, overcoming the limitations of using predefined categorizations such as IPC when representing the content. Based on multiple collaboration and knowledge networks, we then conduct a longitudinal analysis and apply a stochastic actor-oriented model (SAOM) to model network dynamics over different periods. The influence of network features and structures, individual node characteristics, and various dimensions of proximity on collaboration dynamics is tested and analyzed.