The rapid expansion of big data and the increasing complexity of modern computer networks have accelerated the integration of artificial intelligence (AI) into network technologies. This study systematically examines the applications, challenges, and future prospects of AI in computer networks under big data environments. Through comprehensive analysis, the paper explores AI-driven innovations in routing optimization, congestion control, data fusion, network failure prediction, security threat detection, traffic forecasting, and load balancing. It highlights how machine learning, deep learning, and reinforcement learning significantly enhance network intelligence, autonomy, and efficiency, outperforming traditional methods in dynamic and large-scale network scenarios. Furthermore, AI-assisted network design and configuration enable more robust, adaptive, and resilient architectures. Despite these advancements, challenges remain in algorithm transparency, system stability, and talent cultivation. The study proposes directions for future research, including explainable AI, improved robustness, and professional training initiatives. Overall, the integration of AI and computer network
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