Research on the Preprocessing and Postprocessing Procedures in BP Neural Network Applications

As a classical artificial intelligence algorithm, the BP (Backpropagation) neural network is widely applied in fields such as image processing, medical diagnostics, and time‐series prediction due to its powerful nonlinear mapping capability and adaptive learning features. However, its efficient performance heavily relies on preprocessing and postprocessing support. This paper systematically explores the preprocessing and postprocessing procedures in the practical applications of BP neural networks, focusing on the impact of data preprocessing (including data cleaning, normalization, and feature selection) on improving model training efficiency and effectiveness, as well as the role of postprocessing (such as output data smoothing and error correction) in optimizing the accuracy and interpretability of predictions. Additionally, the paper examines two typical application scenarios, namely medical image classification and financial forecasting, to validate the practical effects of preprocessing and postprocessing optimizations. Through this study, best practices for BP neural network preprocessing and postprocessing are summarized, and future research directions involving multimodal data fusion and deep learning technologies are proposed.