This paper presents optimization strategies for deep learning models in natural language processing (NLP) to address challenges such as data heterogeneity, model interpretability, and multilingual transferability. The authors propose improvements from four perspectives: model structure, loss functions, regularization methods, and optimization strategies. Extensive experiments on three tasks—text classification, named entity recognition, and reading comprehension—demonstrate the effectiveness of these strategies. Techniques like Multi-Head Attention, Focal Loss, LayerNorm, and AdamW significantly improve model performance. The paper also explores model compression techniques, such as knowledge distillation, which enable efficient deployment of deep models in resource-constrained scenarios. NLP tasks involve diverse and complex data, posing challenges for deep learning models. Text data contains multiple levels of information, and multilingual and cross-domain applications require models to generalize well. The black-box nature of deep learning models limits their interpretability, making it difficult to understand decision-making processes. Additionally, models often struggle with long-tail distributions and domain-specific knowledge, affecting their adaptability. To address these issues, the paper proposes various optimization strategies. Network structure optimization includes using Transformer models for long-range dependencies and improving generalization through depth and width adjustments. Loss function optimization involves using focal loss for class imbalance and contrastive learning for better representation. Regularization techniques like L2 regularization, dropout, and layer normalization help prevent overfitting. Optimization algorithms such as AdamW and RAdam improve convergence and solution quality. Experiments on datasets like IMDB, CoNLL-2003, and SQuAD show that the proposed strategies enhance model performance. For example, incorporating multi-head attention increased accuracy in text classification, while focal loss improved precision and recall in named entity recognition. Regularization techniques reduced overfitting, and model compression techniques like knowledge distillation reduced parameter counts while maintaining performance. The paper concludes that these optimization strategies are crucial for advancing NLP applications. They improve model performance, accelerate convergence, and reduce complexity, providing valuable insights for future research and practical deployment. The study highlights the importance of addressing data heterogeneity, model interpretability, and multilingual transferability in NLP.This paper presents optimization strategies for deep learning models in natural language processing (NLP) to address challenges such as data heterogeneity, model interpretability, and multilingual transferability. The authors propose improvements from four perspectives: model structure, loss functions, regularization methods, and optimization strategies. Extensive experiments on three tasks—text classification, named entity recognition, and reading comprehension—demonstrate the effectiveness of these strategies. Techniques like Multi-Head Attention, Focal Loss, LayerNorm, and AdamW significantly improve model performance. The paper also explores model compression techniques, such as knowledge distillation, which enable efficient deployment of deep models in resource-constrained scenarios. NLP tasks involve diverse and complex data, posing challenges for deep learning models. Text data contains multiple levels of information, and multilingual and cross-domain applications require models to generalize well. The black-box nature of deep learning models limits their interpretability, making it difficult to understand decision-making processes. Additionally, models often struggle with long-tail distributions and domain-specific knowledge, affecting their adaptability. To address these issues, the paper proposes various optimization strategies. Network structure optimization includes using Transformer models for long-range dependencies and improving generalization through depth and width adjustments. Loss function optimization involves using focal loss for class imbalance and contrastive learning for better representation. Regularization techniques like L2 regularization, dropout, and layer normalization help prevent overfitting. Optimization algorithms such as AdamW and RAdam improve convergence and solution quality. Experiments on datasets like IMDB, CoNLL-2003, and SQuAD show that the proposed strategies enhance model performance. For example, incorporating multi-head attention increased accuracy in text classification, while focal loss improved precision and recall in named entity recognition. Regularization techniques reduced overfitting, and model compression techniques like knowledge distillation reduced parameter counts while maintaining performance. The paper concludes that these optimization strategies are crucial for advancing NLP applications. They improve model performance, accelerate convergence, and reduce complexity, providing valuable insights for future research and practical deployment. The study highlights the importance of addressing data heterogeneity, model interpretability, and multilingual transferability in NLP.