Abstract: The list of environmental permit (LEP) is the core lever of the ecological environment zoning-based regulation (EZR) system, which aims to achieve pollution prevention and control at the source through spatial layout constraints, pollution emission control, environmental risk prevention and control, and resource and energy utilization efficiency control. Quantitatively identifying the objects, control measures, and intensity of LEPs is a crucial step for supporting EZR implementation. However, LEPs face challenges such as extensive policy texts, diverse control measures, and complex expressions. In this study, we utilized unsupervised natural language machine-learning techniques to mine the pattern of control vocabulary in LEPs and multidimensional quantitative labels for text content. Based on this, we employed natural language deep learning models to classify and evaluate the policy content of LEPs. Hebei Province is one of the provinces with the most complete industrial categories and the most complex resource and environmental issues in China, with typical and representative characteristics in ecological environmental regulation. Taking the industrial control measures of LEPs in Hebei Province as an example, we identified 10 categories of policy keyword features and 64 main policy keywords, with a sentence coverage rate of 95% for corresponding keywords in the entire lists. We constructed 24 classification labels for the control measures and industries, and applied and compared the classification recognition effects of BERT, RoBERTa, and ALBERT deep learning models on policy texts. The highest prediction accuracy, recall rate, and F1 score could reach 0.95, 0.79, and 0.86, respectively. The trained models could effectively identify the access control contents. It was found that there were still deficiencies in the clear, specific, and quantitative control measures of LEPs in Hebei Province, and the contents of refined control, assessment indicators, and time limits needed to be supplemented and refined. The method proposed in this study had good applicability prospects. It was recommended to combine cutting-edge artificial intelligence on this basis to further improve the model’s automatic processing efficiency, dynamic analysis, and ability to provide refined policy adjustment suggestions.