Advancements in social security management increasingly rely on intelligent systems capable of adapting to dynamic demographic and economic conditions. Traditional forecasting and adjustment mechanisms often face limitations such as static parameter settings, insufficient temporal learning capacity, and weak responsiveness to multidimensional policy signals. Existing analytical models struggle to integrate long-range prediction with real-time decision adjustment, resulting in reduced allocation efficiency and delayed policy adaptation. The present approach aims to establish a unified predictive–decision framework capable of delivering adaptive contribution and benefit adjustment strategies with enhanced accuracy and stability. Novelty is introduced through a Multi-Channel Access Proximal Policy-driven Temporal Encoder–Decoder Network (MCAPP-TEDN), integrating multi-stream temporal processing with reinforcement-guided optimization and neural networks. The social security adjustment dataset includes 2000 pieces of comprehensive information on social security management, including demographic, financial, and policy-related indicators. Preprocessing involves data cleaning, Min–Max scaling, and Independent Component Analysis (ICA), which enables the extraction of latent temporal representations for efficient encoding. Within the proposed mechanism, the TEDN captures complex multi-horizon dependencies and generates future allocation trajectories, and the MCAPP module refines adjustment strategies through bounded, stable policy updates. Implemented in Python, experiments have demonstrated that the proposed MCAPP-TEDN model outperforms the existing models and achieves better results according to accuracy (0.8165), MAE (0.15), MSE (0.03), RMSE (0.19), and MAPE (3.9) to enhance social security management. These findings demonstrate the capability of the MCAPP-TEDN model to deliver more resilient, dynamic, and data-driven social security adjustment mechanisms.
Yingli Sun (Sun,) studied this question.