Overall Thinking and System Design of Environmental Criminal Law in the Context of Risk Society

Abstract

People are perplexed by the society’s complex and ever-changing risks, which increase their desire for safety. In light of this, and in light of the characteristics of environmental problems and environmental criminal law, this paper attempts to examine some concepts and system design in environmental criminal law using a holistic approach. A user intention understanding model based on attention and CNN (Convolutional Neural Network) is proposed in this paper. The text features are extracted using CNN, and the crime risk is predicted using RF (Random Forest). The user intention understanding model based on attention mechanism and CNN has the best effect, with an accuracy rate of 76.41 percent and a microaverage accuracy rate and recall rate of 75.85 percent, according to the experimental results. The RF prediction accuracy is the highest, at 0.794, which is higher than the SVM (Support Vector Machine) prediction accuracy of 0.784. It demonstrates that RF prediction results are more accurate and superior to SVM prediction results. The RF method’s prediction model can meet the needs of crime risk prediction, so it can be said that it points in the right direction for the effective use of data mining technology in this field.