Exploring the construction of business management model in the context of big data

Abstract

Abstract Big data has made it necessary for business administration to be more intelligent and informationized. This paper introduces data mining technology, explains the specific steps of data mining, and analyzes commonly used data mining algorithms. The rule mining of the C4.5 algorithm is illustrated by using information entropy, the XGBoost model is used as the base learner of Stacking integrated learning and model fusion is carried out. The regional economic prediction model was constructed using the C4.5 rule mining algorithm, while the enterprise credit rating classification model was established using the Stacking algorithm. The empirical evidence shows that the regional economy will be affected by the main body of the enterprise, the industrial structure and the development of the enterprise, in which the industrial structure and the development of the enterprise showed exponential growth in 2007-2018, and their growth rates are all around 30%. Using the Stacking algorithm for enterprise credit rating classification, the recall rate of the weighted fusion model with GRU network as a meta-learner has improved by 2.4%. By analyzing the application of big data technology in business administration data, we illustrate its role in business administration decision-making so as to provide a certain reference for the construction of the business administration informatization model.