Optimal Layout of Urban Green Space System Based on the Coupling Model of Spatial Reconstruction

In the layout of urban planning, the rational design of green land includes the optimal choice of landscape and vegetation, which needs to solve the conflict of local/global optimal solution and understand the rationality of urban green layout with multi-objective natural language. According to the demand for multi-objective optimal solutions in urban green land planning, we study the target deep learning algorithm based on space vector mutation operation. In this paper, we propose to search for a local optimal solution based on Diversity Indicator Double and Niched Local Search Evolutionary Algorithm diversity fitness and filter the information of objective space and decision space through the niche method. It intends to avoid the imbalance between the diversity of goal space and decision space caused by the wrong distance information. By improving genetic diversity, we take the complex changes of gene vector reaction in generating offspring as the research focus. Based on the improvement of multi-objective non-dominated genetic variation, we search for the optimal transfer function relationship in the primary stage of variation to achieve the goal. For high-dimensional planning with constraints, we first select the coupling model to reduce the dimension, and then combine the spatial reconstruction data clustering to complete the clustering. Priority is given to the optimal selection of the output of the non-dominated solution set obtained in each stage to improve the algorithm’s performance. Experiments show that the proposed algorithm, combined with the use of different mutation strategies, outperforms the current mainstream algorithms in solving multi-objective problems of natural language understanding, especially in solving complex relational problems, which is 10.2% and 15.6% higher than other algorithms. The frequency of parameter operation can be updated in real-time, which strengthens the convergence effect of the algorithm. It is proved that the research content of this paper has better application potential in multi-objective spatial decision-making. The research content of this paper can not only achieve intelligent layout optimization for urban green space system but also carry out reasoning for large-scale spatial layout. The effect is higher than the current mainstream algorithm and plays a role in promoting science and technology for future urban construction and long-term planning.