Intelligent Mining and Visualization of Massive Financial Digital Archives Based on Attribute Grouping

With the continuous development of economy, enterprises also put forward higher requirements for financial data management. At present, enterprises will use attribute based grouping model to encode, store, analyze and utilize accounting information, but the research on this method is not enough. Therefore, this paper conducts experiments on this, studies the intelligent mining of massive financial digital archives based on attribute grouping, and expects to propose a new financial archives management method to improve the efficiency of information management. This paper mainly uses experimental and comparative methods to study the financial digital archives management system. After many experiments, the experimental data shows that when the sample ratio is less than 8, the accuracy and recall of the model LFM show a gradual upward trend. These two indicators basically meet the system requirements. This shows that the intelligent mining of massive financial digital archives based on attribute grouping has great help in improving efficiency.