The present work is aimed at solving the difficulty of BC big data information analysis and the defects of traditional BC platform visual interface (VI), such as nonstandard layout, unreasonable color use, unclear guidance, and increased user learning cost. Firstly, this paper expounds on BC technology, the related theory of information visualization (IV), and the IV design method of BC-generated big data. Secondly, by formulating the user experience design strategy, a big data visual information sharing platform (ISP) based on behavior experience (BE) is designed. Finally, the system performance is tested. The results show that (i) the proposed BE-based big data visual ISP has the basic functions of information query and module jump. The overall interface of the platform is simple and tidy, the information layout is reasonable, the presentation method is more intuitive, and the visual effect is better. (ii) The host throughput of each system module when processing business is greater than 100 times/s, and the success rate (SR) of event handling is greater than 99%. The average response time (RT) of terminal processing is less than 0.3 s, and the average RT of the terminal side is less than 0.4 s. The system’s central processing unit (CPU) occupancy rate (OR) shall be controlled below 30%. The memory OR shall be below 30%, both of which are lower than the standard value, and the system performance meets the standard. To sum up, the proposed ISP has basic functions and ensures good operation performance. It is suitable for the IV of BC-generated big data. The purpose is to provide important technical support for the IV of BC-generated big data and improve the efficiency of users’ data information acquisition and analysis.

The emergence of intelligent technology has brought a particular impact and allows for virtuality-reality interaction in the educational field. In particular, digital twins (DTs) feature virtuality-reality symbiosis, solid virtual simulation, and high real-time interaction. It has also seen extended applications to the field of education. This study optimizes the design of the visual communication (Viscom) course based on the deep learning (DL) algorithm. Firstly, the theory of DL is analyzed following the relevant literature, and the typical DL networks, network structures, and related algorithms are introduced. Secondly, Viscom technology is expounded, and DL technology is applied to the Viscom course. Then, the applicability and feasibility of DL in the Viscom course are analyzed through a questionnaire survey (QS) design by collecting students’ attitudes towards Viscom courses before and after the experiment. After introducing DL into the Viscom course, the results show that students’ learning interest and satisfaction with the practical knowledge mastery have increased. However, the satisfaction with theoretical knowledge mastery before practical courses has decreased; overall, the teaching effect of the Viscom course has been improved. Therefore, the introduction of DL into the DT-enabled Viscom can provide a reference for developing the Viscom course. The research content offers technical support (TS) for integrating DT technology and modern education.