Data analysis environment design and landscape design that support heterogeneous models have shown potential in the field of data analysis and landscape design. Integrating different types of models and algorithms, the design environment can provide comprehensive and accurate data analysis and design functions, but its actual effects still need to be further discussed. In this study, empirical research methods are used to construct a data analysis environment that supports heterogeneous models, and related experiments are carried out. In terms of data analysis, different types of data sets are used for tasks such as classification, regression, and clustering. In terms of landscape design, experiments such as terrain analysis, plant selection and material simulation were carried out. At the same time, the resource utilization rate was evaluated. The experimental results show that the accuracy rate of this method is between 91 and 96%, and the data analysis environment design and landscape design that support heterogeneous models have achieved significant improvements in accuracy and performance.