Due to society’s practical demands, the legal profession’s growth within the context of the new liberal arts is necessary to improve teaching mechanisms and the comprehensive development of interdisciplinarity. In this paper, from the two major dilemmas of the specificity of legal instruments and the correlation between sentencing tasks, to examine the relationship between sentencing prediction, charge prediction, and law suggestion of legal instruments, respectively, the Bi-LSTM-attention model, end-to-end memory network model, and CNN-GRU network model are employed, to build an intelligent legal learning system. The outcomes demonstrate that, compared to the conventional machine learning algorithm, the intelligent legal learning system based on deep learning can increase prediction performance by 5.2% to 6%, global accuracy can reach 93.3%, and accuracy of legal documents processing by 7.9%. The deep learning-based intelligent legal learning system suggested in this study can assist law students in completing legal paperwork duties and increase their learning effectiveness.