Integrating Long Short-Term Memory and Multilayer Perception for an Intelligent Public Affairs Distribution Model

In the realm of urban public affairs management, the necessity for accurate and intelligent distribution of resources has become increasingly imperative for effective social governance. This study, drawing on crime data from Chicago in 2022, introduces a novel approach to public affairs distribution by employing Long Short-Term Memory (LSTM), Multilayer Perceptron (MLP), and their integration. By extensively preprocessing textual, numerical, boolean, temporal, and geographical data, the proposed models were engineered to discern complex interrelations among multidimensional features, thereby enhancing their capability to classify and predict public affairs events. Comparative analysis reveals that the hybrid LSTM-MLP model exhibits superior prediction accuracy over the individual LSTM or MLP models, evidencing enhanced proficiency in capturing intricate event patterns and trends. The effectiveness of the model was further corroborated through a detailed examination of training and validation accuracies, loss trajectories, and confusion matrices. This study contributes a robust methodology to the field of intelligent public affairs prediction and resource allocation, demonstrating significant practical applicability and potential for widespread implementation.