## 1 Introduction
On this basis, the positive and negative samples of pattern instances collected are converted to colored UML models in graph form; then, the convolutional neural network VGGNet is employed to train the dataset and extract features, and SVM are used to train the extracted features to obtain the binary classifier for each design pattern. After the classification models are generated, design pattern detection can be performed on unknown systems based on the binary classifier of each pattern. Deep learning technology has advantages, such as powerful automatic feature extraction, while SVM classifiers excel at binary classification problems. Pioneeringly, this paper converts the design pattern detection problem into a graph classification problem and leverages deep learning technology in combination with the SVM to recognize design patterns. In this way, we can realize the end-to-end design pattern detection from system design (here, mainly referring to the system UML model) or source code to design patterns. The experimental results show that the proposed method has a better detection effect compared with non-machine learning design pattern detection methods and the ones based on traditional machine learning algorithms, and can distinguish behavioral patterns with similar structural features. This paper breaks through the effective bottleneck of design pattern detection methods based on graph theory, formal techniques, XML, and other non-machine learning techniques, as well as KNN, decision trees, SVM, and other traditional machine learning algorithms, providing a brand-new idea and direction for future research and development in the field of design pattern detection methods. detection. In addition, this paper also provides a reference idea and solution for UML model correctness and consistency checking, data flow diagram description and checking, software architecture description and checking, and other similar problems. Expanding to the field of reliability and safety of software systems, this paper can introduce the most cutting-edge machine learning technology--deep learning into various aspects such as program correctness proof, automatic testing and BUG repair, code automatic completion, and code analysis, etc., making the field of reliability and safety of software systems truly enter the \"intelligence era\". At present, we have developed a prototype supporting tool system for the above design pattern recognition theory and method. The achievement of this paper can be applied to the detection of design patterns in the software development process of software companies or individuals. Users can more conveniently and quickly detect the corresponding design patterns from software systems, which can improve the quality of the software systems on the basis of saving a lot of human, financial, and material costs. Therefore, this study has both theoretical significance and broad application prospects. Processes such as system division and training of deep learning models are transparent to users. Users only need to master the preliminary knowledge of the object-oriented language or UML and design patterns, and then they can use this method and its supporting tool.