The work aims to improve the prediction accuracy of the external corrosion rate of buried pipeline. The corrosion rate prediction model of buried pipeline was established based on kernel principal component analysis (KPCA) and the general regression neural network (GRNN) optimized by Beetle antennae search (BAS) algorithm. The main factors affecting external corrosion of buried pipeline were extracted by preprocessing the original data through KPCA. GRNN was used to build a mathematical model to predict the external corrosion rate of buried pipeline and BAS algorithm was adopted to optimize the model to reduce the effects of artificially set parameters. In addition, the pipelines buried in natural gas transmission project from Sichuan to East were utilized as an example to analyze 12 key influencing factors and establish the external corrosion index system of buried pipeline. MATLAB-R2014a software was used for simulation processing, and compared with the actual values. The predicted results of the model were basically consistent with the actual values. KPCA could effectively reduce the dimensions of the indicator system and extract three main factors with 97.9% original information, including soil resistivity, oxidation-reduction potential and Cl? content. Thus, the calculation process was simplified. BAS-GRNN model was adopted to improve the prediction accuracy to 7.83%. The average relative error was 5.21%, and the determination coefficient was 0.93. Compared with other models, this model had better performance and higher prediction accuracy. Thus, the main influencing factors extracted by KPC Accord with engineering practice. BAS-GRNN model provides a new idea for the prediction of external corrosion rate of buried pipeline and a reference basis for the maintenance and updating of buried pipeline by higher precision and better adaptability.