High-throughput molecular simulations and machine learning algorithms have been widely used to identify promising metal–organic frameworks for gas separation. However, most studies are limited to screening high-performance materials from existing databases, which fails to fully utilize the predictive function of machine learning. This paper combines genetic algorithms and high-throughput screening to deeply mine anion-pillared MOF (APMOF) performance feature relationships and predict high-performance materials that are not in the database. Considering the actual industrial conditions (CF4/N2 = 10/90), we chose the ratio of CF4:N2 = 1:9 to simulate the adsorption separation of gas mixtures of MOF materials at a temperature of 298 K and a pressure of 1 bar. First, the CF4/N2 separation properties of MOFs in the APMOF library were obtained based on molecular simulations. Then, the filtered data were coded according to the method of “building block classification − structural interval categorization”. Then, the machine learning algorithm was used for model training to obtain a high-precision model. Finally, the tangent adaptive genetic algorithm was used to recombine the genes of the materials, and the new MOF materials were successfully reverse-engineered. The study found that the pore-limit diameter of APMOFs is most conducive to the separation of CF4/N2 by MOFs when the pore-limit diameter of APMOFs is within two times the molecular dynamics diameter of CF4. 134 MOF materials were predicted to have CF4/N2 selectivities exceeding 46.30. The use of organic ligands such as 4,4′-bipyridyl or 1,4-bis(4-pyridyl)benzene (bpb) increases the likelihood of these materials being high-performance for CF4/N2 separation. The combination of computational screening methods and machine learning can expedite the design and development of new high-performance MOFs. This approach can also provide guidance for the synthetic direction and pattern of materials, which can assist experimentalists in their synthesis.