Taking the most strict water resources management in Qujing of Yunnan province as an example,the paper put forward evaluation index system and grading standard of the most strict water resources management and constructed the model of evaluation based on support vector machine regression (SVR) and radial basis function (RBF) neural network.By use of analytic hierarchy process (AHP),it selected 20 indicators from 4 aspects such as total water use, water use efficiency, limiting pollutant and responsibility assessment, constructed the evaluation index system and grading standards of the most strict water resources management; and used the method of randomly generating and randomly selecting to verify the SVR model and RBF model in the most strict water resources management evaluation grade standard threshold between the construction of small capacity of training and testing samples.It evaluated and analyzed the example by use of SVR and RBF model.The results showed that SVR and RBF models have higher evaluation accuracy and generalization ability, and can be used for the most strict water resources management and evaluation.The evaluation results by SVR and RBF model for the most strict water resources management of Qujing in 2010, 2015, 2020 and 2030 are “not ideal”, “more ideal”, “ideal” and “the most ideal”.