# Second-Order Text Matching Algorithm for Agricultural Text
## 1 Introduction  
Agriculture plays a crucial role in the global economy, providing diverse food sources and serving as the economic backbone for many countries [1]. Its importance goes beyond food security and economic development, directly affecting ecological balance, social stability and the global trading system. The sustainable and healthy development of agriculture is essential to ensure human survival, promote global economic prosperity and maintain the ecological balance of the planet. In the current digital era, with the continuous emergence of large text data, there is a growing realisation that harnessing artificial intelligence and big data technologies [2] is necessary to empower agricultural production with science and data [3]. Faced with massive amounts of text data, people need to utilize databases to manage data [4] and text matching technology to quickly and accurately find the information needed to help people make decisions, improve the efficiency of agricultural production, management, scientific research, etc., better disseminate and share agricultural knowledge, and promote the application of agricultural technology. Since its inception, text matching has been a central task in natural language processing, playing a crucial role in information retrieval, question-answering systems, naturallanguage inference [5], and various other areas. In the early stages, from the early 1990s to the late 2000s, traditional rule-based and statistical methods dominated text matching. These included keyword matching, bag-of-words models and TF-IDF algorithm [6] and so on. While effective in certain scenarios, these methods had limited ability to understand semantics and context. By 2010, statistical and machine learning methods were gaining ground. Some classification algorithms such as Support Vector Machines [7], Naive Bayes [8] and Decision Trees [9] were widely applied to text matching. Research in this era focused on feature engineering and model optimisation to improve matching accuracy. From the mid-2010s, with breakthroughs in deep learning, Neural Networks significantly advanced text matching. Models such as Convolutional Neural Networks [10], Recurrent Neural Networks [11] and attention mechanisms [12] improved the capture of semantic information and context, increasing the accuracy of text matching. Pre-trained language models such as BERT and GPT [13] brought new breakthroughs to text matching tasks. In recent years, multimodal matching and cross-language matching [14] have gained popularity, addressing tasks involving text, images and video, and facilitating global information exchange. In the early days, agriculture relied on basic text retrieval systems to access information on agricultural research, market trends and policy. These systems typically used keyword matching, which limited retrieval to superficial information. In the early 1990s, with the advent of information technology, agriculture adopted specialised knowledge bases and decision support systems. These systems integrated and analysed large volumes of agricultural data using text matching technology to support agricultural decision-making, focusing on areas such as pest control, weather forecasting and crop management. In the 2010s, with the rise of Internet of Things (IoT) technology [15], text matching played a crucial role in smart agriculture. Agricultural IoT systems, using sensors and data collection technologies, combined real-time agricultural data with textual information to help farmers make decisions. Text matching was used to correlate disparate data sources, such as matching weather forecasts with agricultural advice. In recent years, agriculture has explored advanced text matching technologies, including Natural Language Processing (NLP) and knowledge graphs [16]. Building agricultural knowledge graphs enables systems to better understand and correlate textual information, providing users with more intelligent agricultural solutions.