With the complexity of environmental problems, the implementation of environmental laws faces many challenges. This paper proposes an intelligent decision support system based on radial basis function network and Takagi Sekino fuzzy model. The radial basis function network has a strong ability to predict the change trend and can efficiently learn and fit the complex data in the environmental law problems. Following the introduction of the TS fuzzy model containing a large number of fuzzy rules, the inference module of the intelligent decision support system is constructed, which is responsible for simplifying the complex nonlinear problem into a linear correlation problem, in order to output a simpler and clearer discretionary result that meets the legal requirements. The research results show that radial basis function network and T-S fuzzy model achieve the most excellent performance compared with the comparison algorithm, and the classification accuracy of T-S fuzzy algorithm is improved by 0.176 compared with the simple fuzzy algorithm, and the output of the system is consistent with the realistic decision-making results on the four major influencing factors of the illegal circumstances, harmful consequences, economic conditions and mental quality, which are highly compatible with the existing legal structure. . The article concludes by adding strategies to improve the legal appropriateness of intelligent decision support systems, which contribute to the objectivity of decision-making on environmental legal issues.