Objective and scientific evaluation methods can ensure reasonable and accurate policy evaluation results and directly and effectively promote policy formulation, implementation, and feedback adjustment. Different from the previous composite evaluation methods, the PMC index model avoids subjectivity and improves accuracy to a great extent. It is a relatively objective quantitative evaluation method in the world that is only used to analyze policy texts. However, at present, the setting of the PMC model index system is relatively fixed. The number of indicators is small, generally at most 10 primary indicators. It is not possible to conduct a comprehensive quantitative evaluation research on the content of this policy paper. Therefore, this project carries out in-depth text mining on the smart energy industrial policy sample set through the tools of ROST CM6 and VOSviewer. According to the mined high-frequency words, network and small group results, the PMC model is optimized, with 17 primary variables and 115 secondary variables, and a 4×4 symmetrical surface diagram of the PMC matrix. The results show that 8 of the 17 policies are good policies and the remaining 9 are acceptable policies. Among them, the guidance on energy work 2021 issued by the National Energy Administration in 2021 is the most comprehensive and scientific. Among the 17 primary variables and indicators, the sample has advantages in policy nature, policy evaluation, research basis, policy openness and key policy contents; There are still some deficiencies in policy scope, incentive guarantee, policy object, policy function, energy service, energy terminal application and energy types; There are still great deficiencies in policy timeliness, policy issuing institutions, policy fields, policy combinations and policy technical tools, which need to be improved.

Policy Quantitative Evaluation, Text Mining, PMC Index Mode, Smart Energy Policy, Carbon Neutral