With the completion of the State Grid Corporation’s maintenance system, the number of substations has increased dramatically, the grid structure has become increasingly complex, and there have been internal and external reasons such as the contingency of emergencies, and equipment failures have occurred from time to time. This paper aims to explore the potential value of massive data, show the laws of business data, and further give full play to the comprehensive support of data for enterprise operation and production management, and promote the realization of intelligent and lean power grid core business. This paper uses power system data to provide reliable data support for equipment defect full cycle management and equipment state analysis through ANOVA and neural network statistical analysis. At the same time, we use Term Frequency-Inverse Document Frequency(TF-IDF)Algorithm to calculate the importance of keywords and construct the power keyword library. By constructing Bayesian text classification model, we can classify the defect parts, defect categories and defect causes automatically. This method can be applied to the construction of power grid production work order text analysis system, improve the data quality and system automation level, help the business department to improve work efficiency and provide the basis for power grid business analysis. This method is applied to the data cleaning of the primary production equipment of power grid enterprises, and the accuracy of data error correction for equipment defects with voltages above 110kV is between 93% and 95%, and good results have been achieved.