Research on Intelligent Enterprise Asset Management Platform: Integrated Multi-Algorithm Financial Analysis Practice

This study proposes an intelligent enterprise asset digital integrated management platform based on deep learning to address the problems of lagging financial decision-making, difficulty in quantifying investment risks, and lack of precision in detecting abnormalities in capital in enterprise asset management. The platform constructs a complete technical system covering financial decision optimization, investment risk warning, and capital flow monitoring by innovatively integrating three types of algorithmic modules, namely, Deep Belief Network-Reinforcement Learning (DBN-RL), Long Short-Term Memory Network-Graph Convolutional Network (LSTM-GCN), and Self-Organizing Mapping-Generative Adversarial Network (SOM-GAN). In financial decision optimization, DBN-RL module adopts three-layer Restricted Boltzmann Machine (RBM500-250-125) stacking structure to extract the deep features of financial data, and combines with reinforcement learning to establish a dynamic decision-making mechanism centered on the 8% reward for profit growth + 6% penalty for risk overruns; in the field of investment risk management, the LSTM-GCN module adopts 128-unit LSTM layers to processing five-year high-frequency investment time-series data and constructing GCN asset correlation maps based on Pearson correlation coefficient (|ρ|>0.5) to realize the quantitative analysis of cross-market risk transmission; in the dimension of capital monitoring, the SOM-GAN module utilizes a 10×10 hexagonal topology SOM network to cluster the capital flow patterns with the help of a four-layer fully-connected generator (FC128-64-32- 16) and a three-layer MLP discriminator for adversarial training to generate the benchmark distribution, and the anomaly is determined with a 0.1 mean square error threshold. Validated by the dataset of CSI 300 listed companies (2018-2023, 12, 450 financial records), the platform significantly improves the decision-making accuracy to 92.3% (21.1 percentage points higher than XGBoost) compared to the traditional method, the investment risk warning F1-score reaches 89.7% (26.3 percentage points higher than the rule engine), and the capital anomaly detection False alarm rate is reduced to 4.2%. The actual deployment cases show that the ROI of a manufacturing enterprise has increased from 8.3% to 11.7% after application, and the average monthly loss of abnormal funds has been avoided by 6.5 million RMB. The core contribution of this study is the establishment of a multi-algorithm synergistic enterprise asset management paradigm, which provides a verifiable technical path for digital transformation.