Aiming at the problems of transmission time delay and network congestion caused by load imbalance in highly “ central” connected multi-campus networks, a dynamic routing optimization algorithm based on adaptive multi-sampling Dueling deep Q-Network (AMD-DQN) is proposed. Firstly, the idea of Dueling DQN is introduced into the network model, and the structure of the multilayer perceptron is improved by centralized processing to prevent high estimation of value function. Then, the experience playback mechanism adopts an adaptive multisampling mechanism, which combines random, nearest and priority sampling methods, adjusts adaptively according to the load situation, and randomly selects the sampling mode according to the weighted probability. Finally, the AMD-DQN network structure is combined with reinforcement learning signal and random gradient descent to train the neural network, and the maximum value action of each step is selected till the transmission is successful. The experimental results show that compared with the traditional DQN and Dueling DQN algorithms, the average delay of the AMD-DQN algorithm is 128. 046 ms, and the throughput reaches 5. 726 / s, which effectively reduces the transmission delay of packets and improves the throughput. At the same time, the congestion degree is evaluated from five directions, and good experimental results are obtained, which further alleviates the congestion of the network.