Abstract
As one of the key subjects of multi-center governance of environmental concerns, public perception is crucial in forming and implementing environmental policy. Based on data science research theory and the original theory of public perception, this study proposes a research framework to analyze environmental policy through network text analysis. The primary contents are bidirectional encoder representation from transformers-convolution neural network (BERT-CNN) sentiment tendency analysis, word frequency characteristic analysis, and semantic network analysis. The realism of the suggested framework is demonstrated by using the waste classification policy as an example. The findings indicate a substantial relationship between perceived subject participation and policy pilot areas and that perceived subject participation is repeating. On this premise, specific recommendations are made to encourage policy implementation.