Acrylamide forms through the reaction between reducing sugars and asparagine in the thermal processing of food. Detection measures like LC-MS, HPLC are time-consuming and costly, which inspired us to use back propagation-artificial neural networks (BP-ANN) based on a genetic algorithm to establish an acrylamide prediction model in fried dough twist. The effects of frying time and temperature on acrylamide contents, as well as the color difference and acid value at different time and temperature were determined. Acrylamide content was found significantly correlated with temperature ( < 0.01) and was correlated with acid value and color difference ( < 0.05). Thus, temperature, acid value, and the color difference were set as input layers, and acrylamide content was set as an output layer to establish a BP-ANN network prediction model. The weight and threshold values in the BP-ANN network prediction model were optimized with a multi-population genetic algorithm and the test data were set to obtain an optimized BP neural network predicting model. The results showed that the Levenberg-Marquardt back-propagation training algorithm of the BP-ANN model with 5 hidden layer neurons and 0.005 learning rate was the best predictive performance, which the correlation coefficients () of test and validation were 0.9640 and 0.8999, suggesting a good fitting and strong approximation ability. The BP-ANN model is expected to accurately predict the content of acrylamide in fried dough twist.