This paper explores the reform of computer network course instruction in the context of rapid technological advancement and interdisciplinary integration. Traditional teaching models are constrained by outdated theoretical content, weak practical components, and limited responsiveness to emerging technologies such as AI, IoT, cloud computing, and edge computing. To address these issues, a collaborative model combining instructor-led design and large language model (LLM) support is proposed. LLMs are used to assist with dynamic content updates, interdisciplinary case generation, and intelligent teaching resource construction. The reform focuses on three major aspects: restructuring curriculum content with real-world applications, integrating interactive learning and hands-on tasks, and aligning theoretical instruction with cross-domain problem-solving. The curriculum reform introduces flexible course structures and AI-supported personalized learning paths, along with the establishment of an online teaching and experimentation platform to sustain continuous updates and student engagement. This framework provides a replicable model for cultivating high-quality network professionals with solid theoretical foundations, practical competencies, and innovative capabilities in the AI era.