Application Research of Neural Network-based Data Assessment and Prediction Method in Statistical Analysis

This article introduces the application of neural networks in evaluation and prediction tasks. Compared with traditional statistical methods and manual experience, neural networks have the characteristics of automatically learning based on data, can extract effective features from massive data, and achieve high accuracy prediction and complex pattern recognition of data through modeling and learning of deep abstract representations. Neural networks are widely used in petroleum data prediction, financial risk assessment, social network prediction, medical diagnosis, and other fields. This article aims to improve the accuracy and efficiency of prediction and decision-making based on the research of statistical evaluation and prediction methods based on neural networks. In addition, this article also introduces the indicator system and evaluation and prediction construction standards. The indicator system mainly describes the relationship between maintenance and indicators and their respective main attributes, including indicators, dimensions, measurement units, and unit indicators. The evaluation and prediction construction standards include production and operation data prediction and data quality prediction. The prediction platform provides various functions to support data prediction, such as prediction indicator management, prediction algorithm management, prediction algorithm simulation, prediction rule engine, production and operation warning, data quality prediction, prediction information release, and prediction problem management. The research and application of this article have important significance for promoting data mining, image recognition, natural language processing, and other fields.