Natural Language Processing (NLP) is widely used in fields like machine translation and sentiment analysis. However, traditional NLP models struggle with accuracy and efficiency. This paper introduces Deep Convolutional Neural Networks (DCNN) into NLP to address these issues. By integrating DCNN, machine learning (ML) algorithms, and generative adversarial networks (GAN), the study improves language understanding, reduces ambiguity, and enhances task performance. The high-performance NLP model shows a 10% improvement in segmentation accuracy and a 4% increase in recall rate compared to traditional models. This integrated approach excels in tasks such as word segmentation, part-of-speech tagging, machine translation, and text classification, offering better recognition accuracy and processing efficiency.

翻译：自然语言处理（NLP）广泛应用于机器翻译和情感分析等领域。然而，传统NLP模型在准确性和效率方面存在不足。本文引入深度卷积神经网络（DCNN）以解决这些问题。通过整合DCNN、机器学习（ML）算法以及生成对抗网络（GAN），本研究提升了语言理解能力，减少了歧义，并增强了任务性能。与传统模型相比，该高性能NLP模型在分词准确率上提升了10%，召回率提高了4%。这种集成方法在分词、词性标注、机器翻译和文本分类等任务中表现优异，提供了更好的识别准确率和处理效率。