Aiming at the problem that Wireless Capsule Endoscopy （WCE） image classification models are only for a single disease or limited to a specific organ， and are difficult to adapt to clinical needs， a WCE image classification model based on improved ConvNeXt-T（ConvNeXt Tiny） was proposed. Firstly， a Simple parameter-free Attention Module （SimAM） was introduced during the model’s feature extraction process to make the model focus on the key areas of WCE images， so as to capture the detailed features such as the boundaries and textures of lesion areas accurately. Secondly， a Global Context Multi-scale Feature Fusion （GC-MFF） module was designed. In the module， global context modeling capability of the model was firstly optimized through Global Context Block （GC Block）， and then the shallow and deep multi-scale features were fused to obtain WCE images features with more representation ability. Finally， the Cross Entropy （CE） loss function was optimized to address the problem of large intra-class differences among WCE images. Experimental results on a WCE dataset show that the proposed model has the accuracy and F1 value increased by 2.96 and 3.16 percentage points， respectively， compared with the original model ConvNeXt-T； compared with Swin-B （Swin Transformer Base） model， which has the best performance among mainstream classification models， the proposed model has the number of parameters reduced by 67.4% and the accuracy and F1 value increased by 0.51 and 0.67 percentage points， respectively. The above indicates that the proposed model has better classification performance and can assist doctors in making accurate diagnosis of digestive tract diseases effectively.

In the customs good declaration scenarios， a classification model needs to be used to categorize the goods into uniform Harmonized System （HS） codes. However， the existing customs good classification models ignore the location information of words in the text to be classified， while the HS codes are in tens of thousands， which leads to problems such as class vector sparsity and slow convergence of the model.To address the above problems， a classification model based on Hierarchical Multi-task Bidirectional Encoder Representation from Transformers （HM-BERT） was proposed by combining the manual hierarchical classification strategy in real business scenarios and making full use of the hierarchical structure feature of HS codes. In one aspect， the dynamic word vector of Bidirectional Encoder Representation from Transformers （BERT） model was used to obtain the location information in the text of customs declaration goods. In other aspect， the accuracy and convergence of categorization were improved by making full use of the category information of different levels of HS codes to perform multi-task training of BERT model. In the effectiveness verification of the proposed model on the 2019 customs declaration dataset of a domestic customs service provider， HM-BERT model improves 2 percentage points in accuracy with faster training speed compared to BERT model， and improves 7.1 percentage points in accuracy compared with H （Hierarchical）-fastText. Experimental results show that HM-BERT model can effectively improve the classification effect of customs declaration goods.