With the rapid advancement of internet, artificial intelligence and big data technologies, intelligent network teaching systems have been widely applied in modern education. Nevertheless, most existing platforms fail to fully utilize massive teaching resources, resulting in low system operating efficiency and poor resource utilization. To address these deficiencies, this paper proposes a personalized network distance teaching system based on big data analysis. The overall architecture and core functional modules of the system are elaborated, and functional and performance tests are conducted through simulation experiments. This system adopts multi-layer architecture and integrates Agent technology and Bayesian network. It mainly realizes two core functions: big data-based personalized resource recommendation and two-way teaching quality evaluation. Experimental results demonstrate that the proposed system can effectively analyze user learning behaviors and push targeted learning resources in real time. Meanwhile, it supports comprehensive evaluation and feedback on students’ learning status and teachers’ teaching performance. In terms of operational performance, the system occupies less memory and CPU resources and responds faster than traditional intelligent teaching platforms. It can well adapt to diverse practical teaching scenarios and possesses broad application prospects in the field of distance education.

Sun He. Design and Implementation of Network and Distance Teaching System. Academic Journal of Computing & Information Science (2026), Vol. 9, Issue 6: 23-28. https://doi.org/10.25236/AJCIS.2026.090604.

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