The regulation of polyacrylonitrile (PAN) copolymer composition and sequence structure is the precondition for producing high-quality carbon fiber high quality. In this work, the sequential structure control of acrylonitrile (AN), methyl acrylate (MA) and itaconic acid (IA) aqueous copolymerization was investigated by Monte Carlo (MC) simulation. The parameters used in Monte Carlo were optimized via machine learning (ML) and genetic algorithms (GA) using the experimental data from batch copolymerization. The results reveal that it is difficult to control the aqueous copolymerization to obtain PAN copolymer with uniform sequence structure by batch polymerization with one-time feeding. By contrary, it is found that the PAN copolymer with uniform composition and sequence structure can be obtained by adjusting IA feeding quantity in each reactor of a train of five CSTRs. Hopefully, the results obtained in this work can provide valuable information for the understanding and optimization of AN copolymerization process to obtain high-quality PAN copolymer precursor.