Abstract
With the gradual strengthening of science and technology and the continuous progress of the times, various fields of technology have been improved, resulting in an increasing amount of data generated year by year. In terms of UML diagram processing, the classification of UML diagrams has always been a hot topic of discussion, and for more complex systems, it has very good expressive power. Therefore, it has been applied in many fields and has become a priority for people to consider. This article will be based on the convolutional neural network graph classification algorithm to obtain data from UML diagrams and partition them. Firstly, analyzing and discussing the content overview of UML, it was found that it has wide applications in various fields; Then, through the research and analysis of convolutional neural networks, a classification model is constructed based on their characteristics. Finally, by collecting data on UML diagrams, a UML diagram dataset is designed. By preprocessing the dataset, the overall iteration count of the model, the UML diagram classification model, the accuracy of classification results, and the impact of average time can be obtained. After analyzing the existing research results, it was found that the graph classification algorithm model mentioned in this article can better analyze and classify UML diagrams.
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Wang, F. Simulation of UML graph classification model by using data preprocessing and convolutional neural network. Opt Quant Electron 56, 277 (2024). https://doi.org/10.1007/s11082-023-05921-3
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DOI: https://doi.org/10.1007/s11082-023-05921-3