Abstract: Machine learning makes data intellectualization processing and full use of knowledge and value contained in data possible. To explore the approaches of intellectual analysis and application in landscape architecture based on machine learning, we carried out three experiments. Two experiments are related to data analysis study, to propose urban colour impression based on investigating image colour clustering analysis, and the landscape visual quality evaluation and network application platform deployment based on image recognition technology. The last experiment is related to digital design creation, to propose the terrain generation method for design scheme comparison, including two subitems, which are the terrain generation with the Generative Adversarial Networks of deep learning as well as predicting the elevation in the unknown area by building masks, respectively. All the three experiments employ the algorithms of classification, clustering, and regression, as well as the Generative Adversarial Networks of deep learning, to propose new study methods based on machine learning for traditional study issues. Therefore, with the application of machine learning to landscape architecture, we can effectively learn about the interaction enhancement knowledge from multi-source, find problems and propose new methods to solve problems.