大数据挖掘技术对数字经济特征的识别优化
广东科技学院, 计算机学院, 广东东莞 523083
| 摘 要： | 大数据技术在各个领域的应用比较广泛，使得数字经济特征识别问题成为研究热点。传统的数据分析无法解决数字经济的特征识别问题，而且特征识别能力较弱。由此，本文提出数据挖掘技术联合大数据的算法，构建特征识别的判断模型。首先，采用传统数据分析方法对大数据进行分类，按照特征进行集合划分，降低不同特征数据的处理复杂性，削弱多维度数字经济信息对特征识别的影响。然后，传统数据分析方法将数据分类，形成不同维度的子特征识别域，并对不同维度子特征识别进行综合判断。借助MATLAB仿真，在识别等级一定的情况下，本文构建算法的准确性、计算时间均优于传统数据分析方法、大数据挖掘技术。在初始特征识别标准和预期效果的条件下，本文构建的算法可以对特征识别进行综合判断，符合数字经济特征识别的需求。 |
| 关 键 词： | 挖掘技术; 大数据; 特征识别; 数字经济 |
| DOI： | 10.57237/j.cst.2023.03.002 |
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收稿日期: 2023-06-23
接受日期: 2023-08-03
在线出版日期: 2023-08-29
Identification and Optimization of Big Data Mining Technology for Digital Economy Characteristics
College of Computer, Guangdong University of Science and Technology, Dongguan 523083, China
| Abstract: | Big data technology is widely used in various fields, making the problem of digital economy feature recognition become a research hotspot. Traditional data analysis cannot solve the problem of feature recognition in the digital economy, and the feature recognition ability is weak. Therefore, this paper proposes an algorithm of data mining technology combined with Big data to build a judgment model for feature recognition. First of all, traditional data analysis methods are used to classify Big data, and Partition of a set is carried out according to characteristics to reduce the processing complexity of data with different characteristics and weaken the impact of multi-dimensional digital economic information on feature recognition. Then, traditional data analysis methods classify the data to form sub feature recognition domains of different dimensions, and make comprehensive judgments on sub feature recognition of different dimensions. With the help of MATLAB simulation, the accuracy and calculation time of the algorithm built in this paper are better than traditional data analysis methods and Big data mining technology under the condition of a certain recognition level. Under the conditions of initial feature recognition standards and expected effects, the algorithm constructed in this article can comprehensively judge feature recognition, which meets the requirements of digital economy feature recognition. |
| Keywords: | Mining Technology; Big Data; Feature Recognition; Digital Economy |
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