###### Abstract
In terms of innovation indicators, the number of personnel with a bachelor's degree or above, R&D expenditure, and number of invention patents in these ten world-class high-tech zones accounted for over 50% of the total in national high-tech zones. These ten world-class high-tech zones have become the pioneers in developing high-tech industries in China, adjusting industrial structures, driving the transformation of traditional industries, and enhancing international competitiveness. On the other side, most other high-tech zones in China have poor performance. Thus, it is of great significance to fully grasp the development characteristics of these world-class parks, understand the significant differences between other parks and world-class parks, and provide guidance for the future development of other parks in order to achieve high-quality development for national high-tech zones and even the whole country. The evaluation of national high-tech zones has always been an important part of the research on high-tech zones. The construction of an evaluation index system plays an important guiding role in the development of high-tech zones. In recent years, the number of national high-tech zones and the related evaluation data have been rapidly increasing [5]. Faced with the enormous and complex evaluation data, it is important to carry out efficient and accurate data analysis. Many studies have been conducted on the construction of comprehensive and innovation evaluation index system for high-tech zones, most of which are based on expert evaluation, statistical evaluation, and other methods [6]. These methods have high professional requirements and strong subjectivity, and they are easily influenced by the subjective factors of the evaluators, thus lacking scientific rigor. With continuous development, more and more applications of machine learning in evaluation have emerged [7; 8; 9]. Compared with previous methods, machine learning algorithms are more objective, not influenced by the subjectivity of evaluators, and have strong adaptability and high accuracy in various scenarios. Therefore, machine learning has achieved great success in evaluation research in many fields. However, in the current research, there are few studies that use machine learning algorithms for high-tech zone evaluation and even fewer studies exploring the application of machine learning in the ex ploration of world-class park characteristic index systems. Apparently, the main limitation can be due to a lack of data. Evidence regarding the performance of high-tech zones in a nation is very limited. As an initial exploration of the application of machine learning in high-tech zone evaluation research, this study introduces the widely used XGBoost algorithm in machine learning. Considering the availability of data, this paper chooses all China's national high-tech zones during the period from 2013 to 2017 as a sample. Based on the relevant statistical data of the Torch Center of the Ministry of Science and Technology, this study analyzes the significant differences between world-class high-tech zones and other national high-tech zones in each year and constructs a characteristic index system for world-class high-tech zones based on the weighted average of five annual feature indexes. This provides guidance for the development of other parks towards world-class parks.