Exploration on Grassroots Party Building Innovation Driven by Big Data

Abstract

Big data has become a disruptive factor in many fields in the digital age, including grassroots party development and political organizing. This study examines the creative use of big data to support grassroots party building initiatives with the goal of defining its effects and possibilities in promoting more effective, flexible, and responsive political systems. This study looks at how big data analytics can be incorporated into grassroots community mobilization, policy formation, and political involvement processes using a thorough analytical methodology. We investigate several case studies where the application of big data tools and methodologies has improved engagement strategies, streamlined decision-making processes, and improved communication between party leaders and voters. The RNN-LSTM algorithm's principle is discussed in this work from an Internet+ standpoint, and the objective function and regularization term are used in the Taylor expansion to maximize the algorithm's objective function. The RNN-LSTM model is then trained to identify its ideal splitting nodes, and the ten-fold cross-validation technique is used to assess the model's performance. This multifaceted research explains how big data encourages creativity in grassroots party mobilization, coalition building, voter engagement, and issue advocacy through case studies, quantitative measures, and qualitative observations. incorporating curriculum thinking's ingrained ideological science into the creation of grassroots party formation to activate curriculum thinking's nurturing function. The study ends with tactical suggestions for incorporating big data into grassroots party formation successfully, opening the door for additional data-driven approaches in governance and political processes. This study adds to the growing body of knowledge about the relationship between digital technology and political innovation, namely at the grassroots level.