Legal Privacy Protection Machine Learning Method Based on Word2Vec Algorithm

This study uses Word2Vec's word vector representation technology to finely capture the semantic relationships of vocabulary in legal texts through the Skip-gram model. By introducing Hierarchical Softmax optimization, a legal privacy protection model based on Word2Vec algorithm is ultimately designed. The results showed that the model performed better than other comparative algorithms in both the macro classification performance (Fl_macro) and the micro classification performance (Fl_micro). In practical legal sensitive word recognition tasks, the accuracy, recall rate, and F1 score of the model reached 92.56%, 88.78%, and 90.62%, respectively. Therefore, the proposed model effectively improved the accuracy of identifying sensitive legal privacy words and providing new methods for the personal information security protection system.