Research on Intelligent Optimization System for Data Mining of Communication Networks

In this paper, an intelligent optimization system based on data mining and reinforcement learning is proposed to improve the voice communication performance of wireless sensor networks through adaptive network coverage optimization algorithm. First, this study uses data mining technology to deeply analyze historical communication data and identify key factors that affect communication quality, such as signal strength, noise level, and network congestion. Then, through reinforcement learning algorithms, the system can autonomously learn and adjust the network configuration to adapt to changing environmental conditions, thereby maximizing communication efficiency and quality. A detailed model simulation is also established. The simulation results show that the system can run stably in a variety of complex environments, and significantly improve the clarity and stability of voice communication. Especially in the network environment with high noise and dynamic change, the system shows superior adaptive ability and optimization effect. The intelligent optimization system based on data mining and reinforcement learning not only improves the communication efficiency, but also greatly reduces the network maintenance cost, and has significant economic benefits and technical advantages.