Semantic Understanding in English Translation Big Data Model Based on Attention Mechanism

Traditional translation models have limitations in understanding complex contextual semantic relationships, especially when faced with polysemous words, implicit meanings, or culturally specific expressions. It is often difficult to accurately capture the semantic information, resulting in inaccurate translation results. To this end, this paper applies a multi-head attention mechanism, which can simultaneously focus on multiple subspaces of the input sequence during the translation process, thereby enhancing the model's comprehensive understanding of semantics. To further optimize the semantic capture capability, this paper uses residual connection technology to make information transmission more efficient, thereby avoiding the gradient vanishing problem in deep networks. In addition, this paper constructs contextual information by combining word embedding and long short-term memory (LSTM) networks to enhance the accuracy of context understanding. LSTM can effectively capture long-distance dependencies in sentences and improve the control of context during the translation process. Finally, the performance of the model is evaluated through experiments by comparing it on multiple language pairs, and the effectiveness of the proposed model is verified. The Transformer model outperforms the other two models in terms of accuracy, recall, and F1-score, especially on the EN->CN language pair, with an accuracy and F1-score of 0.90 and 0.89 respectively. In addition, by analyzing the performance differences between different language pairs, the model's strong generalization ability in multilingual environments is further demonstrated.