This article mainly explores the application method and effect of big data algorithms combining environmental art design and CAD technology in design decision-making and constructs a design decision support system integrating data collection, processing, analysis, display, and decision support. To comprehensively evaluate the newly developed algorithm system's performance, this article utilizes a range of testing metrics such as system throughput, algorithm execution time, resource utilization, and algorithm complexity. The conducted experiments reveal that the system's response time remains consistent across varying loads, while its throughput stabilizes after a certain level of concurrency, indicating robust concurrent processing capabilities. Furthermore, both the CPU and memory utilization rates of the system are maintained at low levels. In addition, the execution time of different algorithms is significantly different under the same input data, and the execution time of this algorithm is the shortest, which shows that this algorithm has the highest efficiency when dealing with the same task. Through analysis, the system can realize the deep mining and effective utilization of the data related to environmental art design and provide scientific and intelligent design suggestions for designers, thus improving the accuracy and innovation of design.