Compressive strength is an important mechanical property of foamed lightweight soil. Accurately predicting and adjusting the compressive strength of lightweight foam soil is of great practical significance for improving construction efficiency. For intelligent control and optimization of foam light soil, this study designed a topology structure including 4 node input layer, 8 node hidden layer and 1 node output layer. The weight and threshold of BP neural network were improved by genetic algorithm (GA) in input layer. Using the four parameters of water-solid ratio, fly-ash ratio, fine aggregate mixing ratio and bubble rate as input parameters and 28-day compressive strength as output parameters, the two models before and after optimisation were validated and compared using mean squared error (MSE), coefficient of determination (R2) and relative error as samples. Based on this, a method for designing the mix ratio based on different performance requirements was established. The results show that compared with BP neural network, the GA-BP neural network has a larger fitness function value and smaller mean square deviation; the fit between the predicted and actual values can reach 0.946, with stronger prediction accuracy and gene-ralization ability; the global search ability of the genetic algorithm also makes up for the defect that BP neural network can easily fall into local optimum, and can better guide the fitting ratio design of the strength prediction of fly ash foam lightweight soil. The GA-BP neural network based strength growth prediction model for foam lightweight soils enables flexible adjustment of the compressive strength of foam lightweight soils, and it is of important refe-rence value for engineering construction.