Multimodal data fusion for intelligent attribution of environmental legal responsibility

With the increasing complexity of environmental problems, it is very important to accurately determine the attribution of environmental legal responsibility. The purpose of this paper is to build an intelligent model for determining the attribution of environmental legal responsibility based on multimodal data fusion, so as to improve the accuracy and efficiency of determination. By fusing multi-modal data such as text, numerical value and image, the model is built through data collection, preprocessing, feature extraction and selection, fusion algorithm implementation and judgment model construction. The experimental results show that the model has excellent performance, with an accuracy rate of 92%, and also has excellent performance in different types of pollution cases, among which the accuracy rate of water pollution cases is 95%. Compared with the traditional single modal data model, this method has obvious advantages. It can be seen that multimodal data fusion can effectively mine complementary information of data, and this intelligent model provides a reliable and efficient solution for determining the attribution of environmental legal responsibility, which is of great significance to promoting the intelligent development of environmental rule of law.