In a distributed node network, the network topology affects important performance metrics, including link-utilisation, throughput and latency. In a large-scale distributed simulation experiments across geographical areas, the network transmission conditions between different distributed nodes vary greatly. To improve the performance of the whole test system, it is necessary to give a more perfect test node deployment scheme. In practice, the optimisation method of manual adjustment is usually used, which cannot guarantee to find the optimal solution. In this paper, we introduce a deep reinforcement learning (DRL) approach aimed at addressing the node deployment challenge in cross-domain collaborative experiments. We employ the Advantage Actor-Critic algorithm (A2C) to optimize the node deployment strategy and identify the most effective solution. The A2C comprises three components: a Validator responsible for validating the accuracy of the generated network topology, a Graph Neural Network (GNN) designed to efficiently approximate topology ratings, and a DRL actor layer. We tested the method in simulation based on a real experimental scenario, and the experimental results demonstrate the feasibility of the A2C algorithm to solve such problems. 150–250 words.