Abstract
The background of the big data era makes enterprise tax management face many opportunities and challenges, in order to improve the management of enterprise capital operation risks and promote the enterprise to take the road of sustainable development. This paper firstly indexes risk names with the help of web crawler technology, establishes data sources, and then circulates the crawler to obtain the required information. Secondly, a hashing algorithm is applied to compress the massive data into a unique and extremely compact section of hash values by means of constant mapping. Then association rules are used to determine the set of frequent risk items, and the values of the two are continuously changed to derive the final predictive analysis. Finally, a capital operation risk prediction and analysis platform is built by combining the above processes. In this paper, the effectiveness of the proposed platform is verified, and the practical results show that the accuracy of the proposed platform for risk prediction discovery is as high as 97%, and the time spent for risk discovery is controlled within 30 minutes. The relevant data results verify that big data technology improves the accuracy of enterprise capital operation risk prediction and analysis while accelerating the speed of risk discovery.
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