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We developed a predictive model for the pipeline friction in the 520–730 m3/h transmission range using the multi-layer-perceptron–back-propagation (MLP–BP) method and analyzing the unit friction data after the pigging of a hot oil pipeline. In view of the shortcomings of the MLP–BP model, two optimization methods, the genetic algorithm (GA) and mind evolutionary algorithm (MEA), were used to optimize the MLP–BP model. The research results were applied to the standard friction prediction of three sections of a hot oil pipeline. After the GA and MEA optimizations, the average errors of the three sections were 0.0041 MPa for the GA and 0.0012 MPa for the MEA, and the mean-square errors were 0.083 and 0.067, respectively. The MEA-BP model prediction results were characterized by high precision and small dispersion. The MEA-BP prediction model was applied to the analysis of the wax formation 60 and 90 days after pigging. The analysis results showed that the model can effectively guide pipe pigging and optimization. There was little sample data for the individual transmission and oil temperature steps because the model was based on actual production data modeling and analysis, which may have affected the accuracy and adaptability of the model.