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In an era of unprecedented technological advancement, the power industry is undergoing a transformative evolution, particularly in intelligent power grid enterprises. The integration of cutting-edge information science and technology has ushered in a new era of automation, informatization, and intelligence within these enterprises. While this progression promises enhanced production, operation, and management capabilities, it also brings forth a daunting challenge: the effective governance of the burgeoning volumes of power data. This paper aims to conduct in-depth research on evaluating the effectiveness of data governance in power grid enterprises based on deep learning to integrate more closely with their business systems. The main objective is to provide effective and convenient intelligent services for decision-making within an innovative power grid enterprise management system and strengthen the data architecture of these enterprises in data management. First, the deep learning neural network's principle structure and training methods are introduced in detail and combined with the deep learning neural network. This is a different evaluation model for the data governance effectiveness of power grid enterprises based on penalty variable weight. The difference probability density of the power difference data series in the power grid is taken as the evaluation index. The evaluation model for the governance effectiveness of different data is modified. Build a different evaluation model of power grid enterprise data governance effectiveness based on punishment and weight change, comprehensively consider the extent to which the data volume of power grid abnormal data in power grid enterprises affects the evaluation of data governance effectiveness, and complete the assessment of power grid enterprise data governance effectiveness based on deep learning. Experimental results underscore the method's efficacy, demonstrating an exceptional accuracy rate of 94%. This empirical validation highlights the method's efficient evaluation process, offering invaluable technical support for enhancing power data management's consistency, precision, and reliability within power grid enterprises. Moreover, comparative analyses against other methodologies, including KNN, SVM, RF, DT, and RNN, reaffirm the superiority of the DNN model, solidifying its outstanding performance. [ABSTRACT FROM AUTHOR]