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 |
| DOI: | 10.57237/j.cst.2023.03.002 |
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Received: 23 June 2023
Accepted: 3 August 2023
Published Online: 29 August 2023
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