The work aims to design an integrated design scheme for campus-oriented innovation services, to offer new approaches to constructing a smart city driven by big data and digital twins and address the persistent issues of data silos and intelligent decision-making in smart city integration design. In this research, a standardized information model for smart campus data was established and Internet of Things (IoT) sensors primarily using structured data were deployed. By leveraging big data and deep learning methods, a smart campus brain was realized. Furthermore, a human-machine integrated visualization platform driven by Web 3D and digital twins was developed, and an operability perspective, feasible and implementable smart campus design was formulated. Starting from the characteristics of big data, the construction status of smart campuses was analyzed. A standardized model for smart campus data was established, and a Web 3D and digital twin-driven visualization platform based on GRU-CNN deep learning for the smart campus brain was designed. Experimental results demonstrated that this system possessed data visualization and intelligent decision-making capabilities, offering users an immersive, multidimensional, and dynamic human-machine integrated interactive experience. The utilization of a big data-driven digital twin visualization platform enables the integrated operation of campus facilities, enhancing the intelligence, efficiency, and user-friendliness of the campus infrastructure, thereby improving overall utilization efficiency. The design of a smart campus integration platform also facilitates the advancement of modern cities by promoting new modes of data interconnection, data visualization, and intelligent service management. It provides technological and theoretical references for the current development of smart cities.