Design and Implementation of a Deep Learning-Based Intelligent Weeding Robot

With the rapid development of internet technology, Chinese text data has experienced explosive growth. Efficient and accurate text classification has become a core requirement in fields such as information retrieval, sentiment analysis, and intelligent recommendation. Problem: Traditional text classification methods (such as TF-IDF combined with SVM) have limited ability to capture semantic context. The mainstream BERT model suffers from large parameter counts, high training costs, and insufficient adaptability to domain-specific vocabulary, making it difficult to efficiently deploy on small and medium-sized Chinese text datasets. This paper’s structure and content: First, the paper reviews the current state of research and shortcomings in existing text classification methods. Second, it proposes an improved BERT model that optimizes the word embedding layer by introducing domain vocabulary pre-training tasks and employs an attention pruning strategy to reduce the number of model parameters. Finally, the paper conducts comparative experiments on a public Chinese text dataset (THUCNews) and a custom domain dataset (Financial News) to validate the performance of the improved model. The experimental results show that the improved YOLOv8 algorithm achieves 96.8% accuracy and 95.7% mAP@0.5, respectively. These improvements are 3.3 and 2.9 percentage points compared to the original YOLOv8, and 7.5 and 8.1 percentage points compared to the lightweight MobileNetV3 model, demonstrating the advantages of the improved model in classification performance and lightweight deployment.