Research on Feature Extraction and Classification for Unstructured Data based on Deep Learning

With the rapid growth of unstructured data such as text, images, audio, and video, traditional data analysis techniques are facing great challenges in dealing with the complexity and high dimensionality of such data. In this study, we propose a multimodal enhanced Transformer model to process and fuse different types of unstructured data by improving the self-attention mechanism and designing a multi-stream input architecture. Firstly, the model adopts a multi-stream input structure, each stream processes a data modality separately, and maps the data of each modality to the feature space of the same dimension through a dedicated preprocessing and coding network to form a unified representation. Subsequently, a cross-modal self-attention mechanism is introduced into the model, which can establish a global dependency between different modalities and automatically learn the correlation between modal features, so as to extract key features more accurately in the classification process. In order to reduce the computational complexity, an optimization algorithm based on sparse matrix is used to enable the model to efficiently process long sequences and high-dimensional data. Experimental analysis on the benchmark dataset shows that the proposed model is superior to the existing methods in terms of accuracy, precision and recall.