## 1 Introduction  
Pesticide registration information is a crucial component of pesticide knowledge bases. Unlike databases focusing on pesticides' chemical properties and toxicological information, such as the Pesticide Properties Database (PPDB) [1], pesticide registration information primarily provides data with practical application value, including information on suitable crops, target weeds, usage methods, and usage restrictions [2]. This information complements databases like the PPDB, enriching the data dimensions of pesticides in practical applications. It can provide important guidance for people working in fields such as agricultural production, food safety, pesticide regulation, and research and development. Due to the large amount of unstructured text data present in pesticide registration information, it is difficult to store, retrieve, and utilize this information. Pesticide entity-relation extraction is a key component in the automated construction of pesticide knowledge bases. It identifies potential relationships between relevant entities within unstructured pesticide registration data to generate structured entity-relation triplets, supporting pesticide knowledge graph construction, knowledge discovery, and question-answering applications [3; 4]. Compared to general corpora, pesticide registration corpora contain a lot of specialized terminology. Pesticide names, serving as head entities, form complex and overlapping triplet relationships with multiple tail entities. Additionally, the typically long texts result in large spans between entities, weakening the dependencies between contexts [5] and increasing extraction difficulty. Furthermore, there is significant variation in entity lengths, with the shortest and longest entities consisting of just one token and nine tokens. This heterogeneity in entity lengths further complicates entity boundary recognition. These characteristics present multifaceted challenges for the automatic extraction of entities and relations from pesticide registration information. To address the complex information extraction tasks associated with these text structures, mainstream methods primarily use a joint extraction framework based on representation learning. This approach typically utilizes deep neural networks to encode sentences and enhances interactions between the entity and relation subtasks using appropriate labeling strategies or diversified vector fusion methods, enabling the simultaneous extraction of entities and relations from the text. Despite significant progress being made in entity-relation joint extraction research in recent years, existing methods still face challenges during joint extraction, including triplet overlap [6], exposure bias [7], and redundant computation [8]. Based on the above discussion, and given the more complex logical structure and higher information density of pesticide registration information, we propose a single-stage entity-relation joint extraction model based on HT-BES multi-dimensional labeling (MD-SERel) to overcome the shortcomings of existing joint extraction methods. The main contributions of this model are as follows:  
(1) For the entity-relation extraction study of pesticide registration information, this study first conducted data collection and preprocessing. Subsequently, entity and relationship types were defined, followed by the manual annotation of 10 entity types and 11 relationship types using a three-round annotation process. Ultimately, these steps resulted in the construction of the Pesticide Registration Dataset (PRD). (2) The model utilizes RoBERTa combined with a multi-head self-attention mechanism to capture the text's deep semantic features. Additionally, syntactic features are extracted using a syntactic dependency tree and graph neural networks, enhancing our understanding of the text's structure.