Data acquisition is a prerequisite for performing big data analytics. However, as the diversity and timeliness of data increase, the complexity of data collection also increases. In this paper, we take enterprise data on a big data investment platform as the research object, and design two data collection models, static data collection based on incremental crawlers and dynamic data collection based on query topic crawlers, for the static and dynamic characteristics of this data. In the experiments, this paper tests the effectiveness of these two web crawler methods and proves that they can collect static and dynamic investment data comprehensively and accurately. Thus, this study provides an effective data collection scheme that helps improve the accuracy and reliability of big data analysis.
The development of today's society is inseparable from the continuous renewal of science and technology. As an important indicator of scientific and technological innovation, patents reflect the core competitiveness of a country. As patent output continues to increase, the quality and value of patents become increasingly important. In this paper, the machine learning method is used to evaluate the quality of electronic information patents and identify high-quality patents from many patents, which can promote patent transfer and transformation, acquisition and custody, transaction flow, pledge financing, and other economic activities. Machine learning is more reliable and scientific than other methods. It is the task of learning corresponding rules and patterns from data and applying them to new data to make predictions.
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