## 7 Discussion
### Conclusions  
1. Verification of Model Performance Superiority: Experimental results show that through the carefully designed Transformer architecture and customized parameter settings, the constructed model achieves an accuracy rate of over 90% in agricultural terminology recognition, significantly outperforming general-purpose models in the agricultural field. This indicates that targeted model optimization can effectively improve the accuracy and practicality of knowledge-based question-and-answer in specific domains, reducing misunderstandings and deviations in knowledge-based question-and-answer, and enhancing the precision of agricultural technology dissemination. 2. Breakthrough in Lightweight Design: Based on the assessment of hardware requirements for model training, to ensure stability and flexibility during the training process, a GPU with 12 GB of video memory can complete the model training task for larger scales or batch processing. The actual obtained model size is only 1.5 GB, and according to the evaluation of hardware computing power required for loading and applying the model, it can run smoothly in a low-configuration hardware environment supporting 4 GB of video memory, lowering the technical threshold for agricultural intelligent services. This achievement proves that lightweight models, while maintaining high performance, can adapt to resource-constrained environments, facilitating widespread deployment and popularization of the model, especially for remote areas and grass-roots agricultural workers, representing a significant technological advancement. 3. Knowledge Fusion in a Bilingual Environment: This study addresses the absence of minority languages in large language models by constructing a high-quality bilingual corpus in Chinese and Uyghur, promoting technological fairness and linguistic diversity inclusiveness. The development of translation tools and data integration strategies not only enrich the agricultural knowledge base but also build a bridge for cross-lingual knowledge sharing. It is the first time that Uyghur has been proposed and experimentally studied as a large language model corpus to construct a large language model, enhancing Uyghur farmers' ability to access information, reflecting the humanistic care and social value of technology. 4. Innovation in Data Processing and Model Training: During the data preprocessing stage, the study innovatively combines OCR technology and text cleaning methods to effectively convert a large number of agricultural professional books into electronic texts, providing rich training data for the model. Meanwhile, through the simplification of traditional characters and custom text tagging strategies, the quality of knowledge graph construction is ensured, enhancing the model's ability to understand context. This methodological innovation provides valuable experience for the development of specific language models in other fields. 5. Utilizing SentencePiece to Improve Uyghur Word Segmentation Accuracy Through Bilingual Data, Including: 1. Using Chinese data to train the model and translating it into Uyghur to create a targeted dictionary;Integrating translation tools to construct a bilingual dictionary, enhancing multilingual processing capabilities;
* Employing the unigram model to balance coverage and efficiency;
* Preserving text format and structure, suitable for complex document processing;
* Precisely controlling word segmentation to maintain textual naturalness;
* Generating a bilingual vocabulary containing 240,000 words, promoting Chinese-Uyghur bilingual information processing technology. * Innovatively applying a context detection and circuit breaker mechanism for text generation, significantly optimizing the quality and novelty of generated text. Features include: 1.