Professional Talent Training System for Landscape Engineering Based on K-Means Algorithm

Abstract

As the economy and society are developing and changing continuously, the garden industry is also developing and changing. The landscape industry has become a major focus of research on how to bring the training methods of the landscape technology profession more in line with the changing times, and how to make the training methods more in line with the needs of the industry by using modern personnel training methods. And it is important to find a good k-means algorithm to match the development of landscape engineering professionals. In this experiment, a combination of telephone interviews and questionnaires was used to ask questions about the landscape engineering profession. The respondents were the principals of the enterprise or relevant technical personnel. They learned what abilities the landscape engineering professionals needed by the society should have, and then the components were appropriate. The teaching system is used to conduct experimental teaching for landscape engineering majors, and there is also a landscape engineering professional control group for comparative analysis. The experimental results show that 27.45% of the students in the experimental group have 60–70 credits, while only 10.34% of the students in the control group have credits in this interval. The gap between the students in the two classes is very large, mainly because the experimental group pays attention to the combination of practice; practice and theory can better promote students’ mastery and application of professional knowledge. Moreover, 66.67% of the students in this experimental group found jobs in their majors. It can be seen from this that this system of cultivating talents for landscape engineering is very useful.