Due to the continuous digitization of cultural heritage, many users can now experience their own museums. However, most digitization work is currently done manually, making it inefficient in terms of time and cost. This manual process underscores the necessity of automation to keep pace with the speed of digitization. Cultural heritage items are categorized into various groups, but the metadata information for each item varies, making it difficult to infer specific classification criteria. This study aims to automate classification by utilizing visual data. We addressed the issue of imbalanced datasets by generating a training dataset for cultural heritage using generative models. A dictionary was created to use appropriate prompts randomly, ensuring consistency in image generation. To validate the performance of the training model using the generated dataset, a multi-label classification model is used. The results demonstrate the potential for automating classification using visual features derived from text in the cultural heritage domain.