## 1 Introduction  
China is a major agricultural country. Approximately 100 million people [1] in China are engaged in the fruit tree industry. The practitioners in the fruit tree industry, predominantly consisting of fruit farmers, is characterized by a large population size, low levels of education, insufficient information technology proficiency, varying technical skills, and high demand for professional knowledge. During the fruit tree cultivation process, the identification and prevention of fruit tree diseases are the most critical concerns for industry practitioners. Fruit tree diseases pose a severe threat to the development of China's fruit tree industry, exerting profound effects on productivity and economic stability [2; 3; 4]. For instance, apple scab and bacterial spot of peach trees can lead to significant reductions in fruit yield, increased cultivation costs, and decreased fruit quality, thereby further impacting the health of consumers and the market price of fruits [5; 6]. Therefore, mastering the latest research findings and knowledge related to fruit tree diseases is extremely important for practitioners. How to acquire and disseminate relevant knowledge tailored to the characteristics and knowledge needs of practitioners is a question of utmost concern to us. Typically, scientists or researchers opt to announce their latest research findings through the publication of research papers, which represent the most advanced research progress and can achieve objectives such as variety breeding and yield enhancement. However, research papers are characterized by their sheer volume and complex content. Fruit tree industry practitioners, particularly fruit farmers, lack the ability to quickly search, process, and comprehend research papers, making it difficult for them to directly and rapidly acquire advanced knowledge that aids in practical fruit production. Therefore, how to rapidly and efficiently extract knowledge about fruit tree diseases from research papers and disseminate it to the frontline of fruit tree industry production is a long-standing issue that needs to be addressed. In recent years, traditional knowledge engineering and knowledge graph methods have been most commonly used for extracting knowledge about fruit tree diseases. These methods primarily leverage feature engineering, machine learning, and deep learning technologies to achieve entity extraction, relationship extraction, event extraction, and general information extraction [7; 8; 9], realizing knowledge extraction in specific domains such as fruit tree industry. Among these, [PERSON] [10] classified entity extraction methods into rule-based, machine learning-based, and deep-learning-based approaches, enabling the extraction of entities such as fruit tree varieties, planting areas, and pest and disease information from textual data. [PERSON] [11] introduced attention mechanisms at the instance level and pooling feature level of PCNN, proposing a remote-supervised relationship extraction model for agricultural pest and disease that effectively improved extraction outcomes. [PERSON] [12] constructed a series of rules in the field of citrus pest and disease, using the Jena inference engine to complete knowledge reasoning. Traditional knowledge engineering methods still have deficiencies in general information extraction, open-ended scenario information extraction, efficient prompt design, and low-resource information extraction, and their implementation is often very cumbersome, characterized by long implementation times and high costs for servers and other equipment. They also require a high level of technical expertise, making it difficult for non-technical personnel (such as fruit farmers) to independently complete the knowledge extraction process. Additionally, the latest findings of scientists and researchers can be conveyed to fruit farmers through offline training, but this method reaches a limited number of people and is also costly, making it an inappropriate approach.