Study on the Matching Algorithm of Turf Grass Introduction Features Based on Big Data Analysis

In recent years, a large-scale cultivation and introduction of turf grass has been carried out in China with a wide application in various urban greening, gardening and real estate business. Considering the wide variety of grass species. it is difficult to decide the introduction of certain grass species for the site. Given the great coverage of land of China, a wide variety of grass species is applicable to be introduced. Taken the following factors into consideration, different regions, climates, lights, moisture and fertilizer demands, their influence by introduced grass species to the grass species survival rate and the growth cycle is supposed to be paid more attention. Based on the big data mining of the experiment to cultivate grass species, this paper extracts key factors and makes them into a time-varying neural network model, which could be exerted to solve the grass species matching problem when introducing. The experimental data verified that the algorithm proposed in this paper could optimize the matching function between the selected grass species and site conditions.