Application of RBF Neural Network in Enterprise Management Data Analysis

In order to improve the efficiency of grassroots enterprise management, based on the 2020 enterprise survey data of a certain county development zone, a combination of rough set and RBF neural network algorithm is used to analyze the three main influencing factors of the enterprise's industrial production value, electricity consumption, and labor consumption, the enterprise level is divided based on the differences between various parameters. The experimental results indicate that: The BP neural network algorithm has advantages such as self-learning ability and nonlinear mapping. In 2020, the sample classification accuracy reached 85.2%, and in 2021, the sample classification accuracy reached 80.8%. In 2022, the classification accuracy reached 86.25%. According to the algorithm's data classification results, the results are relatively close to the enterprise classification situation in a certain county's development zone, indicating that the rough set RBF neural network algorithm is an effective evaluation method in enterprise green development classification; By comparing the visual graphics between the two algorithms, it is found that the classification effect of the rough set RBF neural network algorithm is more obvious than that of the BP neural network algorithm. The results indicate that Based on the rough set RBF neural network algorithm, the enterprise division of a certain county development zone has strong accuracy, high efficiency, and is easy to promote, providing technical guidance for the enterprise management work of a certain county development zone.