As the foundation of AI, deep learning frameworks play a vital role in driving the rapid progress of AI technologies. However, due to the lack of unified standards, compatibility across different frameworks remains limited. Faithful model transformation enhances interoperability by converting a source model into an equivalent model in the target framework. However, the large number and diversity of deep learning frameworks, combined with the increasing demand for custom frameworks, lead to high conversion costs. To address this issue, this study proposes an automatic AI source code migration method between frameworks based on a domain knowledge graph. The method integrates domain knowledge graphs and abstract syntax trees to systematically manage migration challenges. First, the source code is transformed into a framework-specific abstract syntax tree, from which general dependency information and operator-specific details are extracted. By applying the operator and parameter mappings stored in the domain knowledge graph, the code is migrated to the target framework, generating equivalent target model code while significantly reducing engineering complexity. Compared with existing code migration tools, the proposed method supports mutual migration among widely used deep learning frameworks, such as PyTorch, PaddlePaddle, and MindSpore. The approach has proven to be both mature and reliable, with part of its implementation open-sourced in Baidu’s official migration tool, PaConvert.