Research on semantic understanding and text generation algorithm based on deep learning and big data analysis

Traditional semantic understanding methods rely on manual feature engineering, and it is difficult to deal with complex semantic relationships. Deep learning models can automatically learn the structure and semantics of a language by simulating the working mode of human brain neural networks. However, current algorithms still face many challenges, such as accurately understanding semantic information, generating natural and fluent texts, and processing large-scale data efficiently. In order to solve these problems, this paper proposes an innovative double-tower architecture framework, which combines big data feature engineering and deep semantic modeling. The first tower uses platforms such as Hadoop and Spark to preprocess text and extract statistical features. The second tower adopts a model structure based on Transformer, which mixes BERT and GPT features and captures contextual semantic information through self-attention mechanism. The outputs of the two towers are integrated by dynamic weighted stitching, and the integration of statistical features and depth semantic features is realized. In addition, this paper also introduces the prior attention weight generated by TF-IDF matrix, optimizes the self-attention mechanism, and designs a joint loss function to balance the needs of semantic understanding and text generation tasks. The experimental results show that the two-tower fusion model improves the accuracy by 7.3% compared with BERT-base in semantic understanding task, and improves the BLEU-4 value by 20.2% compared with GPT-2 in text generation task, and the dynamic weight mechanism effectively reduces the training time. The ablation experiment further verified the importance of each component to the model performance. The research in this paper provides a new technical path for natural language processing tasks in big data environment, and significantly improves the performance of semantic understanding and text generation.