Aiming at the problems of low efficiency， low accuracy， excessive occupancy of human resources and intelligent classification algorithm miniaturization deployment requirements in China Customs risk control methods at this stage， a customs risk control method based on an improved Butterfly Feedback neural Network Version 2 （BFNet-V2） was proposed. Firstly， the Filling in Code （FC） algorithm was used to realize the semantic replacement of the customs tabular data to the analog image. Then， the analog image data was trained by using the BFNet-V2. The regular neural network structure was composed of left and right links， different convolution kernels and blocks， and small block design， and the residual short path was added to improve the overfitting and gradient disappearance. Finally， a Historical momentum Adaptive moment estimation algorithm （H-Adam） was proposed to optimize the gradient descent process and achieve a better adaptive learning rate adjustment， and classify customs data. Xception （eXtreme inception）， Mobile Network （MobileNet）， Residual Network （ResNet）， and Butterfly Feedback neural Network （BF-Net） were selected as the baseline network structures for comparison. The Receiver Operating Characteristic curve （ROC） and the Precision-Recall curve （PR） of the BFNet-V2 contain the curves of the baseline network structures. Taking Transfer Learning （TL） as an example， compared with the four baseline network structures， the classification accuracy of BFNet-V2 increases by 4.30%，4.34%，4.10% and 0.37% respectively. In the process of classifying real-label data， the misjudgment rate of BFNet-V2 reduces by 70.09%，57.98%，58.36% and 10.70%， respectively. The proposed method was compared with eight classification methods including shallow and deep learning methods， and the accuracies on three datasets increase by more than 1.33%. The proposed method can realize automatic classification of tabular data and improve the efficiency and accuracy of customs risk control.