Legal text classification and intelligent recommendation system integrating deep learning in the field of environmental law

With the increasing number of environmental laws and regulations, it is urgent to realize efficient text classification and accurate intelligent recommendation. This paper aims to build a text classification and intelligent recommendation system of environmental laws and regulations that integrates deep learning (DL) to improve the efficiency and accuracy of information processing in the field of environmental law. By collecting the text data of environmental laws and regulations, using DL technology, a text classification model based on long-term and short-term memory network (LSTM)-Attention and an intelligent recommendation system combining content-based and collaborative filtering are designed. The experimental results show that the accuracy of the classification model on the test set is 90%, and the average error is about 3.6. The recommendation correlation rate of the recommendation system reaches 82%. This proves that the system has good performance, can effectively handle the texts of environmental laws and regulations, and provides a reliable solution for information management and utilization in the field of environmental law.