China is one of the countries most severely affected by typhoon disasters globally, and Guangxi is one of the regions in China most severely affected by typhoons. On average, Guangxi is affected by typhoons five times a year, with a maximum of nine times. Typhoon disasters have brought huge losses to the Guangxi region and seriously hindered the high - quality development of Guangxi's social economy. In this study, 40 historical typhoon disaster cases that affected Guangxi, caused certain economic losses, and had relatively complete records from 2001 - 2020 were selected. Starting from the three index layers of hazard - causing factors, disaster - bearing bodies, and disaster prevention and mitigation capabilities, 12 typhoon - influencing factors were taken as the input elements of the neural network model. Four kinds of neural networks combined with the cubic spline interpolation method were used for data amplification to construct an intelligent prediction model for typhoon disaster losses in Guangxi. Taking Typhoon Mujigae as an example, the practical application potential of this model was explored to realize the dynamic prediction of typhoon disaster losses in Guangxi. The results show that the GA - BP(genetic algorithm - back propagation) neural network model has the best performance. The R - squared value of the model training set is 0.9847, the RMSE(root mean squared error) is 0.4573, the R - squared value of the test set is 0.9603, and the RMSE is 0.8295. The prediction results are relatively close to the actual typhoon disaster loss situation, which proves the effectiveness of this model.