Research on Software Data Model Optimization Based on Deep Learning Algorithms

In response to the problems faced by traditional software data model optimization in high-dimensional heterogeneous data scenarios, such as inefficient semantic association mining, insufficient cross domain migration ability, and poor dynamic adaptability, this study proposes an automated optimization framework that integrates deep learning to achieve collaborative improvement of model complexity, query performance, and semantic consistency. The framework adopts a three-layer architecture of “feature learning structure optimization dynamic adaptation”: local features and global relationships are extracted through convolutional neural networks (CNN) and graph neural networks (GNN), candidate model structures are generated using generative adversarial networks (GAN), and a deep transfer learning module is introduced to solve the distribution differences of heterogeneous data. Multi objective loss function joint optimization model for entity redundancy, relationship complexity, and query response time, with adaptive mechanism supporting real-time adjustment of the model in dynamic business scenarios. Experiments on 15 public datasets and enterprise order data show that the framework can reduce the number of entities by $30 \%-35 \%$, the number of relationships by $28 \%-32 \%$, shorten the average query response time by $40 \% -48 \%$, and improve semantic accuracy (F1 score) by $23 \%-27 \%$ in cross domain scenarios. The research results have broken through the dependence of traditional methods on manual rules, providing a technical path for the intelligent design of enterprise level data models. Its core methods can be extended to scenarios such as real-time data governance and cross domain model migration. Future research will focus on privacy protection modeling and lightweight model compression under federated learning, further enhancing the engineering application capability of the framework.