Artificial intelligence big model refers to utilizing technologies based on artificial intelligence to integrate multiple technologies and process natural language through AI technology, mainly including deep learning, reinforcement learning, etc. In practical applications, it is necessary to process data scientifically and reasonably to ensure the effectiveness of large models in the field of natural language processing. At present, a large number of large models have emerged in the field of natural language processing, which can analyze and understand text, speech, images, etc., improving the level of natural language processing technology. The rapid development of artificial intelligence technology has promoted the widespread application of large-scale language models in the field of natural language processing. This article studies the specific application of analytical algorithms in AI intelligent language processing, focusing on the experimental design and results of deep learning based natural language processing models, especially in sentiment analysis and text generation. The experiment used a preprocessed parallel corpus of Chinese and English for training, and conducted multiple experiments on the GRU neural network to evaluate its performance in text generation efficiency, semantic accuracy, and application scalability. The results showed that in the evaluation of cross domain text generation effectiveness, this article received a score of 0.87, the cross language generation quality evaluation was 0.86, and the generalization accuracy reached 0.88. Under different parameter settings, GRU neural networks exhibit good training error optimization and generation efficiency. In terms of semantic accuracy, the experiment showed a high BLEU score and grammar error rate control, indicating the model's ability to maintain semantic accuracy and logical coherence. The application scalability evaluation demonstrated the good performance of the model in cross domain and cross language generation tasks. In summary, this study provides important insights and empirical support for a deeper understanding and promotion of the application of artificial intelligence in language processing.