In order to solve the problem that quantifying the area of tank defects detected by ultrasonic wave, an improved quantitative model of tank corrosion defect area is proposed. This model uses the feature importance of XGBoost to initialize the parameters of artificial neural network (ANN) a priori to improve the ANN model. The model can converge faster and improve the accuracy. Design an experimental platform according to national standards, obtain experimental signals, and extract the statistical features of the signals to obtain a data set. Use the data set to train and test improved models, and compare them with traditional models. The experimental results show that the improved ANN model can converge faster and quantify the defect area accurately. Compared with the ANN quantization model, the accuracy in the training set has been improved by 17. 9%, reached 98. 3%. and increased by 16. 6% on the test set, reached 92. 2%.