Optimizing the Operation Mechanism of Public Data Assets and Data-Driven Decision Models in the Digital Economy With Deep Neural Networks

With the rapid development of digital economy, the scale and complexity of public data assets are increasing, and how to efficiently and accurately mine and utilize these data has become a key issue. Existing methods are insufficient in dealing with multimodal data fusion and temporal feature capture, which is difficult to meet the needs of complex public decision-making. To this end, this paper proposes ReLSTM-Opt, a hybrid deep learning model that fuses ResNet and LSTM, to achieve automatic hyperparameter adjustment through Bayesian optimization, and to enhance the model's integrated learning ability for spatial and temporal features. Experimental results show that ReLSTM-Opt significantly outperforms traditional methods in classification accuracy and temporal prediction performance on multiple public datasets, demonstrating good generalization ability and stability. The model provides strong technical support for intelligent analysis and data-driven decision-making of public data assets and promotes the efficient utilization of public data resources in the digital economy.