Intelligent Analysis of Dongguan Red Culture Data Combining Multi-layer Convolutional Neural Network and GAN

With the development of digital technology, the intelligent analysis of red cultural data has become an important topic in the protection and inheritance of cultural heritage. However, due to the diversity and complexity of red cultural data, how to effectively process and analyze these multimodal data is still a difficult problem to be solved. To this end, this paper proposes a method combining multi-layer convolutional neural networks (CNN) and generative adversarial networks (GAN) for intelligent analysis of Dongguan red cultural data. Through the feature extraction ability of CNN and the data generation advantage of GAN, this paper constructs an efficient deep learning model to achieve multi-task learning for image classification, text sentiment analysis, and image generation. Experimental results show that the method combining CNN and GAN performs well in image recognition and data enhancement, especially in improving classification accuracy and enhancing the diversity of the training data set. Specifically, the CNN model based on GAN-enhanced data achieves an image classification accuracy of 94.3%, which is 4.3% higher than the traditional CNN model; in text sentiment analysis, the F1 score of the CNN+GAN fusion model is 0.91, significantly outperforming the LSTM model with an F1 score of 0.82; in image generation tasks, the images generated using GAN-enhanced data show high similarity with real images, with a Frechet Inception Distance (FID) value of 25.5. Compared with traditional methods, this approach significantly improves the robustness and generalization ability of the model and has strong practical application value. The proposed method not only provides a new technical solution for the intelligent analysis of red cultural data but also demonstrates high efficiency and accuracy in multimodal data processing and generation tasks, opening up new research directions for the digital protection and inheritance of cultural heritage.