With the rapid development of artificial intelligence technology in recent years, Internet companies are increasingly demanding for the allocation and optimization of server resources. This article proposes an intelligent algorithm based on reinforcement learning (RL) to improve the selection and configuration process of artificial intelligence servers. By analyzing and comparing existing server selection modes, the RL algorithm model proposed in this chapter can dynamically optimize the allocation of server resources, in order to improve computational efficiency and reduce energy consumption. The experimental results show that the RL algorithm model is superior to the traditional method in many key performance indicators, providing a more efficient server configuration strategy for Internet companies. In the benchmark performance comparison experiment, the average response time of the RL algorithm was 200 ms, and the processing capacity reached 400 requests per second. In the dynamic load adaptability experiment, the RL algorithm can stabilize at 400 requests per second when the load fluctuates. In the fault recovery experiment, in the case of a sudden server shutdown, the RL algorithm only takes 20 min to recover from the fault to the fully functional state. In the final long-term operational efficiency experiment, the RL method showed a monthly improvement in performance and a decrease in operating costs over three months of operation. From the above experimental conclusions, it can be seen that the server configuration method based on RL algorithm outperforms traditional methods in various performance indicators, demonstrating its broad potential and advantages in practical applications.