## 1 Introduction  
Categorization risk is a major concern in customs tax risk management worldwide, as it is fundamental to maintaining tariff security across various countries. In the context of China, accurately identifying misclassifications of goods codes is particularly crucial for safeguarding against risks such as tax evasion and regulatory breaches. Currently, China's customs primarily rely on traditional manual inspection methods to mitigate goods categorization risks, which presents several challenges, including time-consuming and labor-intensive processes, a vast number of goods to inspect, inconsistent experience among personnel, and varying judgment standards. The application of natural language processing (NLP) technology can facilitate the rapid and precise categorization of goods, significantly improving the efficiency of customs procedures and enhancing risk prevention and control capabilities. This paper conducts research on the classification of import and export goods based on the declaration text information, focusing on the Harmonized System (HS) code as the classification target. As shown in Figure 1, import and export goods must be declared by filling in the text of a good's description (Declare Goods Specification) and the HS code. The goods description text contains detailed information about goods, allowing customs to accurately identify and classify them under the correct HS codes. These descriptions are often formatted as semi-structured short texts, with different parts of descriptive information separated by \" |\", referred to as declaration elements in customs. The accurate classification of goods into HS code is essentially a multi-class classification problem, where the goal is to match HS code based on the description text of the goods. This problem can be generalized to various scenarios involving the classification of semi-structured short text, such as medical text classification [1], e-commerce product classification [2], and more. Semi-structured short texts typically comprise multiple parts that may not exhibit clear semantic correlations, leading to issues such as uneven information density and complex contextual relationships. Additionally, in the task of HS code classification for goods, the description text may contain various Chinese terminology or nouns due to the diverse fields involved. Most existing classification models for goods utilize Word2 Vec and BERT (Bidirectional Encoder Representations from Transformers, BERT) for textual representation [3; 4; 5; 6; 7]. However, these models often fall short in effectively capturing key feature information and global dependencies within the text. In addition, previous studies have shown that a hybrid neural network consisting of multiple neural network models can make full use of the advantages of different models to improve the classification performance [8; 9]. In this paper, we propose a hybrid neural network model ERNIE-BiLSTM-Channel attention-Spatial attention (EBLCS), to improve goods classification performance, which effectively utilizes the advantages of the different models. The contributions of this article are as follows:  
* We employ the knowledge-enhanced semantic representation model ERNIE (Enhanced Representation through Knowledge Integration) to represent the goods text. This approach improves the model's ability to capture multi-domain Chinese professional vocabulary, resulting in a more effective and accurate representation of semantic features. * We introduce multi-scale attention to enhance the Bidirectional Long Short-Term Memory Network (BiLSTM) model's ability to perceive scattered key information within text sequences. Channel attention dynamically adjusts the importance of different word embedding dimensions in the text representation vector, achieving a more accurate global contextual representation. Subsequently, spatial attention focuses  
Figure 1: Example of goods declaration information.