Research on Quality and Safety Risk Identification of Import and Export Toys Based on the WOA-BP Model

Within the scope of customs risk management, the weakness in the ability to identify quality and safety risks of import and export products is a key factor contributing to the persistently high product recall rate. To effectively improve this situation, this paper selects import and export toy products as research samples and constructs a research system framework of "data collection - risk classification - risk identification". This study establishes a quality and safety risk identification model for import and export toys based on two machine learning techniques, namely Latent Dirichlet Allocation (LDA) and neural networks. Firstly, Use Python to preprocess the information and employ the ROSTCM software to conduct word frequency analysis to obtain a network relationship diagram. Based on the Dirichlet distribution topic model and toy safety-related indicators, keywords are extracted to determine toy safety risk factors as well as toy safety risk events. A safety risk identification model for import and export toys is established through the BP neural network, and the Whale Optimization Algorithm (WOA) is used to optimize the model. The results of the simulation study show that in terms of the model's accuracy, the prediction accuracy rate of the WOA-BP model is 95.71%, which is 5.71 percentage points higher than that of the BP model. In terms of the model's regression performance, the R-value of the WOA-BP model's test set is 0.997. The WOA-BP model is superior to the BP model and its prediction results are more in line with the actual situation, enabling it to better accomplish the task of identifying quality and safety risks.