The design of 3D scenes should obey the rules of the architecture's organization. At present

3D scene designs are typically carried out by art an designer who lacks knowledge of architecture. A method is proposed in this paper to solve this problem. We extracted the flat and facade features of a scene and analyzed the features by interactive genetic algorithm (IGA). A new method is introduced to evaluate the weights and scores of these characteristics

as well as obtain the adaptive values to optimize the organization of the old structure. After evolution and learning our algorithm

we can expand and reconstruct the old scene to a new scene

which has better organizational form.The 3D reconstruction scene realize the personality style and provide a better experience to users. This method has significance in the field of 3D game design

historical remains recovery

and landscape design. Adaptive Resonance Theory neural network is introduced to extract the features of 3D scenes. The ant colony algorithm is then utilized to optimize the layout of the scene

and introduce the interactive genetic algorithm to obtain the best adaptive individuals to form a larger scene. The algorithm is combined with the intuition and psychological characteristics of the designer

which is realized by the algorithm. The principle of the method is based on the approximation model

which maps the 3D scene to human psychological space. The optimal solution is obtained by the adaptive values of evaluation. To avoid the problem of individual fatigue

we evaluate the information of samples to train the fitness function and obtain an approximate model for updating information in the process of evolution. The method uses a neural network to clutter the feature of 3D models and effectively decrease the work of manual evaluation. This study used a series of specific scenes and extracted features of the scene based on the user evaluation to expand the original scene. The neural network method is used to realize the reorganization and extraction of features. In addition

ant colony algorithm is utilized to reorganize and expand the 3D scene. After using interactive genetic algorithm

we realize the expansion and reconstruction of the old scene. This research analyzed the optimization design of 3D scenes and proposed the idea on how to reconstruct and expand the complex 3D scenes. A hierarchical decomposition method is presented to optimize the complex scene and search each layer to maximize the value of the symmetry characteristics. Based on these symmetry features

we can realize the reconstruction

and by using the ant colony algorithm

we would obtain the optimized layout scheme. The IGA is introduced to obtain the optimal solutions to the scene. Through the optimization of IGA

we can obtain more accurate adaptive values. Optimal individuals can be generated and more optimized design scheme will be obtained. This method can quickly generate a large scene with the original feature and symmetry

as well as realize the expansion and reconstruction of the old scene. Moreover

it mixed features of the local 3-D structure without manual layout and design. The deficiency is that the user satisfaction of the reconstructed scene and the manual organization scene still require substantial experiments for verification. A disadvantage in this method is that it does not perform well enough to explore all kinds of implicit aesthetic indexes. For the analysis of the aesthetic characteristics and style of the 3-D scene

it is impossible to establish a realistic approximate model for quantitative analysis in the present stage. In-depth studies and further efforts should be devoted to solve the problems above. Experiments show that this method is practical and effective; it can effectively improve the efficiency of the design and enhance the ornamental value. This method has practical significance in 3D game design

virtual restoration

and visual simulation.