With the development of ICT (information and communications technology) and IoT (Internet of Things) technology and the promotion of data publicity, the breakthrough and application of data acquisition, processing and visualization technology, the world has entered the era of big data. Landscape Architecture, as an applied discipline, is becoming more and more important to quantitatively measure the function and use of green space. Big data makes up for the shortage of traditional data, and provides a scientifi c and effective research basis for green space planning and design. Therefore, the application of big data in green space planning and design has been paid more attention to, and has launched exploration and practice. Based on the clue of the development of multi-scale application of big data in green space planning and design, this paper summarized the sources and application of big data in green space planning and design, the research route of big data, the multi-scale application of big data in green space planning and design, the limitations and improvement measures of big data, and the application trend of big data in green space planning and design. According to the time characteristics of data generation, this paper divided data into realtime collected activity data and post-uploaded activity information data. The former can also be divided into spatial location data and behavior trajectory data. The data characteristics and application modes were analyzed in this paper. Traditional research methods of landscape architecture are mostly based on sampling data, which is totally different from the research route and process of big data. The research of big data adopts as many data as possible. Its research process can be divided into four steps: determine big data type, data acquisition and integration to form data resources, data mining and analysis, data result analysis. Under the background of smart cities construction, massive, high-precision and effective human activity data can be used to analyze and solve the problems in green space planning and design at different scales, and can be used to construct the research mechanism of multiscale urban green space planning and design. According to the scale differences and application characteristics of big data applications, this paper divided them into three levels: macro scale, meso scale and micro scale. On the macro scale, big data can be used to study the feature of green space usage on urban scale, the factors affecting the use of green space, and the site selection of green space; on the meso scale, big data can be used to guide the planning and design of landscape architecture, such as greenway planning and scenic spot planning; on the micro scale, big data can be used to analyze people’s behavior in green space, and guide the design and transformation of green space. Although big data has the characteristics of high accuracy, wide coverage and fast updating compared with traditional data, it still has limitations in the accuracy of data attributes, the accuracy of data points location and the comprehensiveness of data information. In the future, it can be improved and optimized through data processing, application of multi-source data and supplement of traditional research methods. In addition, the paper analyzed the development of the application of big data in the field of green space planning and design, and found that it has gone through three stages: phenomenal description, planning guidance and causation analysis. With the maturity of big data mining and processing technology, the improvement of data accuracy, and the breakdown of barriers to large data acquisition. Based on data, the construction of green space planning decision-making model is an important direction for future development. And the core issue is to simulate the logical relationship between urban population, land use, construction, transportation, industry and urban green space. The model can evaluate various possible spatial policies in the future planning and development of urban green space through scenario analysis, and provide scientific decision-making suggestions for the construction and timing of urban green space system. In a word, the application of big data in green space planning and design still has a lot of potential. Planning designers and researchers should strengthen the researches on the mining and application of big data according to the characteristics and needs of the times, so as to make it more effective in influencing urban green space planning and construction decisions.