With the rapid development of the Internet and the growth of business requirements, the traditional single system is faced with dual challenges of maintenance and expansion due to its huge code base and highly coupled modules. The existing microservice migration methods rely on subjective judgment or single dimensional analysis, which makes it difficult to fully capture system characteristics. This article proposes a microservice reconstruction scheme based on multi feature fusion, integrating source code and runtime data to construct an inter class dependency undirected weighted graph model, extracting microservice modules through clustering analysis, and supporting database splitting strategies and distributed transactions to ensure data consistency. The core innovation includes a multi feature fusion mechanism that integrates semantic similarity, code structure dependencies, and runtime interactions; Develop the microservice extraction tool MicroRefactor, which significantly improves efficiency and accuracy through a three-stage process; Provide database splitting and consistency assurance solutions. Experimental verification shows that this scheme achieves efficient and accurate module extraction in JPetStore system and medium-sized e-commerce system. After reconstruction, the system functions are complete and performance optimization is significant. The current research is limited by the need to improve code automation processing capabilities and relies on manual design of test cases. In the future, we will explore automated processing technology and test case generation methods to further improve the refactoring plan.

Yuxin Wu. Software Engineering Practice of Microservice Architecture in Full Stack Development: From Architecture Design to Performance Optimization. Machine Learning Theory and Practice (2025), Vol. 5, Issue 1: 64-75. https://doi.org/10.38007/ML.2025.050107.

[1] Jordanov J , Petrov P , Kuyumdzhiev I ,et al.Domain-Driven Design in Cloud Computing:. NET and Azure Case Analysis[J].TEM Journal, 2025, 14(1).DOI:10.18421/TEM141-05.

[2] Dobrinin M V .Extending RESTful web service resources in a JAVA-component-driven-architectureapplication:US18424015;US202400018424015;US202418424015A;US202418424015[P].US12238159B2;US2025012238159B2;US12238159B2;US12238159[2025-08-13].

[3] Tang S , Chen J , Wang D ,et al.MVDiffusion++: A Dense High-Resolution Multi-view Diffusion Model forSingle orSparse-View 3D Object Reconstruction[C]//European Conference on Computer Vision.Springer, Cham, 2025.DOI:10.1007/978-3-031-72640-8_10.

[4] Jadcherla S , Burnam M H .PHYSIOLOGICAL SENSING DEVICE HAVING DATA ACCESS LAYER, AND METHODS OF OPERATION OF SAME:USUS2024/041912;US202400000041912;US2024041912W;WO2024US41912[P].WO2025/035147A2;WO2025000035147A2;WO2025035147A2;WO2025035147[2025-08-13].

[5] Dai Z , Wang S , Lu Q ,et al.Nonlinear hysteresis system control based on sliding mode neural network and observer[J].Transactions of the Institute of Measurement & Control, 2025, 47(10).DOI:10.1177/01423312241279496.

[6] Zhu P. Construction and Experimental Verification of Automatic Classification Process Based on K-Mer Frequency Statistics[C]//The International Conference on Cyber Security Intelligence and Analytics. Cham: Springer Nature Switzerland, 2024: 391-400.

[7] Zhang Y. Research on Optimization and Security Management of Database Access Technology in the Era of Big Data[J]. Academic Journal of Computing & Information Science, 2025, 8(1): 8-12

[8] Pan Y. Research on the Design of a Real-Time E-Commerce Recommendation System Based on Spark in the Context of Big Data[C]//2025 IEEE International Conference on Electronics, Energy Systems and Power Engineering (EESPE). IEEE, 2025: 1028-1033.

[9] Yan J. Analysis and Application of Spark Fast Data Recommendation Algorithm Based on Hadoop Platform[C]//2025 Asia-Europe Conference on Cybersecurity, Internet of Things and Soft Computing (CITSC). IEEE, 2025: 872-876.

[10] Wu, H. (2025). The Commercialization Path of Large Language Models in Start-Ups. European Journal of Business, Economics & Management, 1(3), 38-44.

[11] Xiu L. Analyses of Online Learning Behaviour Based on Linear Regression Algorithm[C]//2025 IEEE International Conference on Electronics, Energy Systems and Power Engineering (EESPE). IEEE, 2025: 1333-1338.

[12] Huang J. Resource Demand Prediction and Optimization Based on Time Series Analysis in Cloud Computing Platform[J]. Journal of Computer, Signal, and System Research, 2025, 2(5): 1-7.

[13] Cai, Y. (2025). Research on Positioning Technology of Smart Home Devices Based on Internet of Things. European Journal of AI, Computing & Informatics, 1(2), 80-86.

[14] Xu D. Design and Implementation of AI-Based Multi-Modal Video Content Processing[J]. European Journal of AI, Computing & Informatics, 2025, 1(2): 44-50.

[15] Zhu, Z. (2025). Cutting-Edge Challenges and Solutions for the Integration of Vector Database and AI Technology. European Journal of AI, Computing & Informatics, 1(2), 51-57.

[16] Huang, J. (2025). Reuse and Functional Renewal of Historical Buildings in the Context of Cultural Heritage Protection. International Journal of Humanities and Social Science, 1(1), 42-50.

[17] Lai L. Data-Driven Credit Risk Assessment and Optimization Strategy Exploration[J]. European Journal of Business, Economics & Management, 2025, 1(3): 24-30.

[18] Lu, C. (2025). The Application of Point Cloud Data Registration Algorithm Optimization in Smart City Infrastructure. European Journal of Engineering and Technologies, 1(1), 39-45.

[19] Ye, J. (2025). Optimization and Application of Gesture Classification Algorithm Based on EMG. Journal of Computer, Signal, and System Research, 2(5), 41-47.

[20] Zhu, Z. (2025). Application of Database Performance Optimization Technology in Large-Scale AI Infrastructure. European Journal of Engineering and Technologies, 1(1), 60-67.