Research on the Application of Mediation Model Based on Deep Learning in Dispute Resolution

This study explores the application and practical effects of a mediation model based on deep learning in dispute resolution. Traditional dispute resolution methods are limited by human resources and time costs, whereas deep learning technology provides new possibilities for dispute resolution with its ability to automatically extract data features and perform efficient learning and prediction. This study constructs an Attention-based LSTM (Long Short-Term Memory) model to achieve automated analysis and processing of dispute cases, aiming to improve the efficiency and accuracy of dispute mediation. Experiments show that the constructed Attention-based LSTM model can achieve 92.5% accuracy in the verification set when dealing with the dispute mediation task, and has high performance in the evaluation indexes such as recall, precision and F1 value. The model can not only accurately classify dispute cases as mediation success or mediation failure, but also capture key events and emotional tendencies in the text, providing valuable reference for the mediation process. In addition, the influence of different parameter settings on the performance of Attention-based LSTM is also discussed, and the advantages of Attention-based LSTM in dealing with long texts and complex semantic relationships are further verified through comparative experiments with other text classification models. The introduction of attention mechanism enables the model to focus on the key information in the text, thus improving the understanding ability of complex and long-length texts. This is particularly important when dealing with social conflicts and disputes, because these events often involve a lot of background information and detailed descriptions. The main contribution of our research is to develop a novel mediation model based on deep learning, which significantly improves the efficiency and accuracy of dispute classification. By automating the classification process, we aim to reduce the workload of mediators and reduce the possibility of human error in event classification. The mediation model based on deep learning shows great potential and application prospect in dispute settlement. This study not only provides a new automatic and intelligent means for dispute mediation, but also opens up a new direction for the application of deep learning in the legal field.