The risk assessment methods of agricultural supply chain finance (SCF) are explored to reduce agricultural SCF’s credit risks. First, the genetic algorithm (GA) is utilized to adjust and determine the initial weights and thresholds of the backpropagation neural network (BPNN), which assesses the credit risks. Second, for the problem that many factors affect the credit risks and the difficulty in selecting the characteristics, the principle of assessment indicator selection is proposed; the characteristics of these indicators are selected by principal component analysis (PCA). Finally, the case analysis method is utilized to verify the proposed risk assessment method, and an optimal credit risk assessment method is established. The results show that GA-BPNN can accelerate the convergence speed of the BPNN and improve the disadvantage in easily falling into the local minimum of BPNN. The PCA method simplifies the complexity of assessment indicator selection, and the representative indicators in agricultural SCF credit risk assessment are successfully selected. Through verification, it is found that the GA-BPNN algorithm performs well in credit risk prediction of agricultural SCF, and its prediction accuracy and prediction speed are improved. Therefore, the used GA-BPNN has performed well in the credit risk prediction of agricultural SCF, which applies to financial credit risk assessment to reduce the credit risks in agricultural SCF.

探索农业供应链金融（SCF）风险评估方法，降低农业供应链金融的信用风险。首先，利用遗传算法（GA）来调整和确定用于评估信用风险的反向传播神经网络（BPNN）的初始权重和阈值。二是针对影响信用风险的因素多、特征选择困难的问题，提出了评价指标选择原则；这些指标的特征是通过主成分分析（PCA）选择出来的。最后，利用案例分析法对提出的风险评估方法进行验证，建立最优信用风险评估方法。结果表明，GA-BPNN 可以加快BPNN 的收敛速度，改善BPNN 容易陷入局部最小值的缺点。PCA方法简化了评估指标选取的复杂性，成功选取了农业SCF信用风险评估中具有代表性的指标。通过验证发现GA-BPNN算法在农业供应链金融信用风险预测中表现良好，其预测精度和预测速度都有所提高。因此，所使用的GA-BPNN在农业供应链金融的信用风险预测中表现良好，适用于金融信用风险评估，以降低农业供应链金融的信用风险。成功筛选出农业供应链金融信用风险评估中的代表性指标。通过验证发现GA-BPNN算法在农业供应链金融信用风险预测中表现良好，其预测精度和预测速度都有所提高。因此，所使用的GA-BPNN在农业供应链金融的信用风险预测中表现良好，适用于金融信用风险评估，以降低农业供应链金融的信用风险。成功筛选出农业供应链金融信用风险评估中的代表性指标。通过验证发现GA-BPNN算法在农业供应链金融信用风险预测中表现良好，其预测精度和预测速度都有所提高。因此，所使用的GA-BPNN在农业供应链金融的信用风险预测中表现良好，适用于金融信用风险评估，以降低农业供应链金融的信用风险。