Aiming at the problems of the traditional Fuzzy Neural Network (FNN) evaluation model in the ser? vice quality evaluation of the National Quality Infrastructure (NQI) comprehensive service information platform, such as slow convergence speed and likely falling into the local optimal solution, a fuzzy neural network intelligent evalua? tion method based on Optimized Principal Component Analysis (OPCA) and Improved Genetic Algorithm (IGA) was proposed. In order to improve the network convergence speed of FNN, OPCA was used to delete redundant index fac? tors reduce the amount of network input, and realize the dimensionality reduction of network input, according to the correlation between evaluation indexes. Then, IGA is combined with FNN, and the coefficients of the membership function of FNN are searched globally by using adaptive crossover and mutation probability, so as to overcome the problem that FNN is easy to fall into local extremum in intelligent evaluation effectively. Based on the actual service quality survey data of the NQI platform in China, the experimental analysis shows that the OPCA-IGAFNN evalua? tion model has a more efficient and accurate evaluation effect.