Research on Automatic Classification and Key Information Extraction Algorithm of Case Documents Based on Deep Learning

In this paper, we propose an innovative deep learning architecture that integrates the deep context understanding capabilities of BERT with the spatial and temporal feature capture advantages of ConvLSTM in processing sequential data. We first use BERT to perform embedding representation learning on case documents to capture deep semantic relationships between words. Then, by introducing the ConvLSTM layer, the model can further analyze the structured information and time dependence inside the document, which is particularly critical for understanding complex case documents. By combining the advantages of the two, an end-to-end solution from text preprocessing, embedding generation, classification and information extraction is realized. It can effectively and automatically classify case documents and accurately extract key information, which improves the efficiency of automatic processing.