Abstract: To address the issues of long cycles and high costs in the traditional exhibition hall layout design, we propose a method of integrating rule-based and data-driven automatic exhibition space planning and layout generation. Firstly, the specified space is divided according to the exhibition hall design rules, and the space is optimized considering beautification and connection. Then, the visual saliency of the exhibition hall space is calculated, and the position and arrangement of exhibits are selected using a priority queue. Next, using the data generated by rule-based methods as a dataset, and the layout search results from the regional center as input, we achieve the diverse generation by combining semantic prediction network and topological prediction network. Finally, through the prediction model, the optimal placement positions of exhibits are determined in decreasing order of importance. On the exhibition hall dataset, we perform comparative experiments with other methods, including HouseGAN++, Deeplayout, and DCGAN. The results indicate that the proposed method has higher visual similarity, with an average reduction of 22.64% in graph edit distance, and an average increase of 25.33% in connectivity, validating the effectiveness of this method.