Research on the Model of Multimodal Learning in Digital Art Education

This paper proposes a multimodal adaptive convolutional neural network (MA-CNN), which aims to improve the learning performance of complex tasks by efficiently fusing multiple modal data. The core innovation of this network lies in the introduction of an adaptive convolutional layer, which can dynamically adjust the fusion weights according to the characteristics of different modal data, thereby optimizing the feature fusion process. Furthermore, a self-supervised learning mechanism is proposed to improve the learning capability of the model without labeled data. To test the validity of MA-CNN, a large number of experiments have been carried out, and the data set contains multiple multimodal information including image, text, audio and video. The results show that the MA-CNN model is more accurate and stable than the conventional single mode model, and the precision of the model is over 15%. Experiments show that multimodal data fusion not only improves the performance of the model, but also improves the robustness of the model. The combination of adaptive convolutional layers and self-supervised learning mechanisms provides a new idea for multimodal learning. This method has shown excellent performance in complex tasks in multiple fields, has wide application potential, and provides valuable reference for future multimodal learning research.