In the current increasingly intelligent wireless communication field, optimizing and adjusting the spectrum resource management goals of wireless communication can greatly increase the transmission rate and energy efficiency of the network. So, how to optimize the allocation of wireless communication spectrum resources is a key issue that needs to be urgently solved in the current network field. This article studied the use of intelligent algorithms for resource allocation in wireless communication using 5G technology, aiming to use deep reinforcement learning algorithms to improve transmission rate and energy efficiency. This article used cellular network resource allocation algorithms and heterogeneous high-density network resource allocation algorithms through model establishment and experimental analysis. Based on the data results, The final results indicated that the wireless communication resource allocation system based on heterogeneous high-density network algorithms has higher energy utilization and faster network transmission rate under a certain number of subchannels.