A Personalized Mining Algorithm for Grassroots Network Data Based on Deep Learning

Abstract To enhance the accuracy and efficiency of mining grassroots network data and to better support practical applications, this study proposes a personalized mining algorithm for grassroots network data based on deep learning. A multi-module neural network architecture is designed to process, filter, and transform raw grassroots network data. Through data preprocessing and hierarchical refinement, the algorithm generates high-precision, structured datasets suitable for personalized mining tasks. A five-layer neural network-comprising an input layer, convolutional input layer, hidden layer, convolutional output layer, and prediction output layer-is constructed to support an integrated training and testing workflow. A redundancy-elimination rule is introduced to prune unnecessary neural network parameters, followed by a maximum weight extraction rule to guide the personalized mining of grassroots network data. Experimental results demonstrate that the proposed algorithm achieves high convergence speed during training and offers superior performance in testing accuracy, enabling precise and reliable mining of high-dimensional and heavily interconnected data. This work lays a technical foundation for the effective utilization and intelligent analysis of grassroots network data.