To evaluate and study the vulnerability of furniture supply chain, in order to provide reference for furniture supply chain risk management decisions from the perspective of supply chain vulnerability.

Based on the new characteristics of furniture supply chain development in the context of the new era, the vulnerability of each link in the furniture supply chain was decomposed, and the Delphi method is used to screen the factors of furniture supply chain vulnerability. SPSS software wass used for reliability and validity analysis, and a furniture supply chain vulnerability evaluation index system including digital supply chain maturity is constructed. Utilizing the advantages of BP neural network in risk assessment, combined with permutation feature importance algorithm, Python was used for simulation training to construct a furniture supply chain vulnerability evaluation model based on BP neural network.

1) a furniture supply chain vulnerability evaluation index system consisting of 3 primary indicators, 9 secondary indicators, and 24 tertiary indicators was constructed; 2) The permutation feature importance algorithm was used to calculate the weights of the evaluation indicators for furniture supply chain vulnerability. Based on the weights, the evaluation indicators were ranked and it was found that the five indicators of market demand prediction, supply chain information collaboration, supply chain decision-making level, industrial structure adjustment, and product competitiveness have significant positive significance for predicting the vulnerability of furniture enterprise supply chains; 3) Through iteration and training, it was found that the furniture supply chain vulnerability evaluation model based on BP neural network has a classification prediction accuracy of 100% for 61 sets of training data, with a maximum relative error of 0.002 256%; The classification prediction accuracy for 20 sets of test sample data is 95%, with a maximum relative error of 0.5%.

The furniture supply chain vulnerability evaluation model based on BP neural network has good nonlinear mapping and learning ability, strong classification and prediction function, and can comprehensively and effectively evaluate the vulnerability of furniture supply chain.